Prof Brendan Englot, from Stevens Institute of Technology, discusses the challenges in perception and decision-making for underwater robots – especially in the field. He discusses ongoing research using the BlueROV platform and autonomous driving simulators.

Brendan Englot

Brendan Englot received his S.B., S.M., and Ph.D. degrees in mechanical engineering from the Massachusetts Institute of Technology in 2007, 2009, and 2012, respectively. He is currently an Associate Professor with the Department of Mechanical Engineering at Stevens Institute of Technology in Hoboken, New Jersey. At Stevens, he also serves as interim director of the Stevens Institute for Artificial Intelligence. He is interested in perception, planning, optimization, and control that enable mobile robots to achieve robust autonomy in complex physical environments, and his recent work has considered sensing tasks motivated by underwater surveillance and inspection applications, and path planning with multiple objectives, unreliable sensors, and imprecise maps.

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Marine environments present a unique set of challenges that render several technologies that were developed for land applications completely useless. Communicating using sound, or at least as people use sound to communicate, is one of them.

Michael Fulton tackles this challenge with his presentation at ICRA 2022 by using body language to communicate with an AUV underwater. Tune in for more.

His poster can be viewed here.

Michael Fulton

Michael Fulton is a Ph.D. Candidate at the University of Minnesota Twin Cities. His research focuses primarily on underwater robotics with a focus on applications where robots work with humans. Specifically, human-robot interaction and robot perception using computer vision and deep learning, with the intent of creating systems that can work collaboratively with humans in challenging environments.

Anthony DiMare and Charles Chiau deep dive into how Bedrock Ocean is innovating in the world of Marine Surveys. At Bedrock Ocean, they are developing an Autonomous Underwater Vehicle (AUV) that is able to map the seafloor autonomously and at a high resolution. They are also developing a data platform to access, process, and visualize data captured from other companies at the seafloor.

Bedrock Ocean is solving two problems in the industry of Marine Surveying.
1. The vast majority of the seafloor is completely unmapped
2. The data that is captured from the seafloor is not standardized or centralized.

Seafloor data conducted by two different companies with the same or different hardware to capture the data can vary significantly in the calculated seafloor profile

Anthony DiMare
Anthony previously founded Nautilus Labs, a leading maritime technology company advancing the efficiency of ocean commerce through artificial intelligence. While at Nautilus, Anthony helped global companies solve challenges with distributed, siloed maritime data systems and built the early team that launched Nautilus Platform into large publicly listed shipping companies.

Charles Chiau
Charles, Bedrock’s CTO, was previously at SpaceX where he helped design the avionics systems for Crew Dragon. He also was a system integration engineer at Reliable Robotics working on their autonomous aviation system and was the CTO of DeepFlight where he worked on manned submersibles including ones for Tom Perkins, Richard Branson, and Steve Fossett.

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In this episode, Abate follows up with Benjamin Pietro Filardo, founder of Pliant Energy Systems and NACROM, the North American Consortium for Responsible Ocean Mining. Pietro talks about the deep sea mining industry, an untapped market with a massive potential for growth. Pietro discusses the current proposed solutions for deep sea mining which are environmentally destructive, and he offers an alternative solution using swarm robots which could mine the depths of the ocean while creating minimal disturbance to this mysterious habitat.

Benjamin “Pietro” Filardo
After several years in the architectural profession, Pietro founded Pliant Energy Systems to explore renewable energy concepts he first pondered while earning his first degree in marine biology and oceanography. With funding from four federal agencies he has broadened the application of these concepts into marine propulsion and a highly novel robotics platform.

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Abate interviews Benjamin “Pietro” Filardo, CEO and founder of Pliant Energy Systems. At PES, they developed a novel form of actuation using two undulating fins on a robot. These fins present multiple benefits over traditional propeller systems including excellent energy efficiency, low water turbulence, and an ability to maneuver in water, land, and ice. Aside from its benefits on a robot, Pietro also talks about its advantages for harnessing energy from moving water.

Watch this episode on YouTube

Benjamin “Pietro” Filardo
After several years in the architectural profession, Pietro founded Pliant Energy Systems to explore renewable energy concepts he first pondered while earning his first degree in marine biology and oceanography. With funding from four federal agencies he has broadened the application of these concepts into marine propulsion and a highly novel robotics platform.

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Emily – short for Emergency Integrated Lifesaving Lanyard – is a remote-controlled rescue boat used by lifeguards to save people’s life at sea (Photo: Hydrolanix – EMILY robot)

This article was first published on the IEC e-tech website.

Rapid advances in technology are revolutionizing the roles of aerial, terrestrial and maritime robotic systems in disaster relief, search and rescue (SAR) and salvage operations. Robots and drones can be deployed quickly in areas deemed too unsafe for humans and are used to guide rescuers, collect data, deliver essential supplies or provide communication services.

Well-established use

The first reported use of SAR robots was to explore the wreckage beneath the collapsed twin towers of the World Trade Center in New York after the September 2001 terrorist attacks. Drones and robots have been used to survey damage after disasters such as the Fukushima Daiichi nuclear power plant accident in Japan in 2011 and the earthquakes in Haiti (2010) and Nepal (2015). Up to now, more than 50 deployments of disaster robots have been documented throughout the world, according to the Texas-based Center for Robot‑Assisted Search & Rescue (CRASAR).

Robin Murphy, head of CRASAR and author of the book Disaster Robotics, says:

The impact of earthquakes, hurricanes, flooding […] is increasing, so the need for robots for all phases of a disaster, from prevention to response and recovery, will increase as well.

Eyes in the sky

Drones, also known as unmanned aerial vehicles (UAVs), can be used to detect and enter damaged buildings, assisting rescue robots and responders on the ground by speeding up the search for survivors through prioritizing which areas to search first. The more quickly SAR teams respond, the higher the survival rate is likely to be. Rescue drones create real-time maps by taking aerial surveys and send back photos, videos and sensor data to support damage assessments.

Drones used for SAR and disaster relief are most commonly powered by rechargeable batteries and are operated autonomously through onboard computers or by remote control. Their equipment typically comprises radar and laser scanners, multiple sensors and video and optical cameras as well as infrared cameras that are used to identify heat signatures of human bodies and other objects. This helps rescuers to locate survivors at night and in large, open environments and to identify hot spots from fires. Listening devices can pick up hard-to-hear audio, while Wi-Fi antennas and other attachments detect signals given off by mobile phones and plot a map that outlines the locations of victims.

New technologies in use or development for rescue drones and robots include ways of increasing survivor detection. Sensors scan areas for heartbeats and breathing, multisensor probes respond to odours or sounds and chemical sensors signal the presence of gases.

Standards put safety first

Much of the technology used in drones comes from commodity electronics developed for consumer essentials like mobile phones. Drones also require global positioning satellite (GPS) units, wireless transmitters, signal processors and microelectromechanical systems (MEMS). The flight controller also collects data from barometric pressure and airspeed sensors.

IEC International Standards produced by a range of IEC Technical Committees (TCs) and Subcommittees (SCs) cover the components of drones such as batteries, MEMS and other sensors, with an emphasis on safety and interoperability.

IEC TC 47: Semiconductor devices, and its SC 47F: Micro electromechanical systems, are responsible for compiling a wide range of International Standards for the semiconductor devices used in sensors and the MEMS essential to the safe operation of drone flights. These include accelerometers, altimeters, magnetometers (compasses), gyroscopes and pressure sensors. IEC TC 56: Dependability, covers the reliability of electronic components and equipment.

IEC TC 2: Rotating machinery, prepares International Standards covering specifications for rotating electrical machines, while IEC TC 91: Electronics assembly technology, is responsible for standards on electronic assembly technologies including components.

IEC SC 21A: Secondary cells and batteries containing alkaline or other non-acid electrolytes, compiles International Standards for batteries used in mobile applications, as well as for large-capacity lithium cells and batteries.

Ideal for isolated and remote hard-to-access areas

Using drones is useful not only when natural disasters make access by air, land, sea or road difficult, but also in isolated regions that lack accessible infrastructure. Recently, drones have started delivering medical supplies in areas where finding emergency healthcare is extremely difficult. In 2014, Médecins Sans Frontières piloted the use of drones to deliver vaccines and medicine in Papua New Guinea. In 2016, the US robotics company Ziplinelaunched a drone delivery service, in partnership with the government of Rwanda, to supply blood and medical supplies throughout the mountainous East African country. Zipline says its battery-powered drones can fly 120 km on a single charge to deliver medicine speedily, without the need for refrigeration or insulation.

Rwanda launches world’s first drone service to deliver blood to patients in remote areas of the country (Photo: Zipline)

A project by a company in the Netherlands to help refugees who get into difficulty in the Mediterranean Sea offers another example of drones being used for humanitarian purposes. Its search and rescue (SAR) drone is intended to fly over long distances, detect boats and drop life jackets, life buoys, food and medicine if necessary.

Currently only about a quarter of the world’s countries regulate the use of drones. Their deployment in disaster relief operations poses challenges involving regulatory issues, particularly when decisions are made on an ad hoc basis by local and national authorities. Humanitarian relief agencies also warn of the risks of relief drones being mistaken for military aircraft.

Two arms good, four arms better

Japan and the US lead the world in the development of rescue and disaster relief robots. Teams from both countries collaborated in recovery efforts after an earthquake and tsunami hit Japan in March 2011, causing a meltdown at the Fukushima nuclear power plant. While a Japanese team operated an eight-meter long snake-like robot fitted with a camera, the US contribution included two remote-controlled robots. The first was a lightweight 22 kg model previously used for bomb disposal and other military tasks before being reconfigured for disaster relief operations. The larger US model, capable of lifting up to 90 kg, was adapted from a device originally used for firefighting and clearing rubble.

Endeavor Robotics 510 Packbots are deployed in emergency situations when direct human intervention is dangerous or impossible, such as after industrial or nuclear accidents. These robots were used at the Fukushima nuclear power plant (Photo: Endeavor Robotics)

In 2017, Japanese researchers unveiled a prototype drone-robot combination for use in disaster relief work. It consisted of a vision-guided robot equipped with sensitive measuring systems, including force sensors, and a drone tethered to the robot. Four fish‑eye cameras mounted on the drone capture video of overhead views, allowing the robot’s operator to assess damage in the surrounding area.

Another Japanese rescue robot unveiled in 2017 is a multi-limbed robot that is 1,7 m tall, with four arms capable of independent operation and four caterpillar treads for mobility. Called The Octopus, it is capable of lifting 200 kg with each arm, crossing uneven terrain and lifting itself over obstacles with two arms while the other two clear debris.

Octopus, developed by Japan’s Waseda University a robot designed to clear rubble in disaster areas including nuclear facilities (Photo: Waseda University)

In the US, researchers are exploring ways in which a small and light collapsible, origami‑inspired robot, first produced by the National Aeronautics and Space Administration (NASA), could be adapted for use as a rescue robot. The device, known as PUFFER (Pop‑Up Flat Folding Explorer Robot) is designed to pack nearly flat for transport, and then re‑expand on site to explore tight nooks and crannies which are inaccessible to larger robots.

Over and underwater too

Given that 80% of the world’s population lives near water, maritime robotic vehicles can also play an important role in disaster relief by inspecting critical underwater infrastructure, mapping damage and identifying sources of pollution to harbours and fishing areas. Maritime robots helped to reopen ports and shipping channels in both Japan and Haiti after major earthquakes in 2011 and 2010 respectively.

In the Mediterranean, a battery‑powered robotic device first developed for use by lifeguards to rescue swimmers has been adapted to help rescue refugees crossing the Aegean Sea from Turkey. This maritime robot has a maximum cruising speed of 35 km/h and can function as a flotation device for 4 people.

Easy to find components and technology
Rescue robots use components and technology found in most other robots used for commercial purposes. Actuators and other electric motors, accelerometers, gyroscopes and dozens of sensors and cameras providing 360⁰ views enable these robots to maintain balance while moving over uneven ground covered with rubble or debris, and to get a sense of the environment around them.

A robot operating in a hazardous environment needs independent power and sensors for specific environments. It may be cut off from its human operator when communication signals are patchy. When remote operation guided by sensor data becomes impossible, a rescue robot needs the ability to make decisions on its own, using machine learning or other artificial intelligence (AI) algorithms.

Several IEC TCs and SCs cooperate on the development of International Standards for the broad range of electrotechnical systems, equipment and applications used in rescue robots. In addition to IEC TC 47: Semiconductor devices and IEC SC 47F: Microelectromechanical systems, mentioned above, other IEC TCs involved in standardization work for specific areas affecting rescue and disaster relief robots include IEC TC 44: Safety of machinery – Electrotechnical aspects; IEC TC 2: Rotating machinery; IEC TC 17: Switchgear and controlgear; and IEC TC 22: Power electronic systems and equipment.

Where humans fear to tread
The number of disasters recorded globally has doubled since the 1980s, with damage and losses estimated at an average of USD 100 billion a year since the start of the new millennium, according to the Overseas Development Institute (ODI), a UK think tank. This trend is likely to lead to increased demand for unmanned robotic devices that can assist disaster relief operations on land, in the air and at sea.

Robots of all kinds play a growing role in supporting SAR teams. Increasing autonomy will create more capable ground robots, while a combination of rapid technological advances and regulation should see the market for disaster relief drones soar over the next five years.

MarketsandMarkets, a research company, estimated in October 2016 that the total global market for drones, comprising commercial and military sales, would grow at a compound annual growth rate (CAGR) of nearly 20% between 2016 and 2022 to exceed USD 21 billion. Drones designed for humanitarian and disaster relief operations will account for 10% of the future drone market, according to the US‑based Association of Unmanned Vehicle Systems International (AUVSI).

We have just taken another momentous step in the journey to unveil the hidden wonders of our own planet!

Since the launch of the Shell Ocean Discovery XPRIZE at the American Geophysical Union Fall Meeting in San Francisco in December 2015, individuals from around the world have been racing to form Teams and develop a range of groundbreaking technologies to access the deep-sea. Registration closed at the end of September 2016 with 32 bold Teams stepping forward to take on the challenge of mapping and imaging our ocean as never before.

Today, we announce the 21 semi-finalists Teams advancing in the Ocean Discovery XPRIZE. These innovative semi-finalist Teams, consisting of almost 350 individuals from 25 countries, represent a broad, impressive diversity of backgrounds and expertise, including middle and high school students, university students, maker-movement enthusiasts, and water and ocean industry professionals.

This diversity is also reflected in the array of technologies and techniques the semi-finalist Teams are developing. Some are proposing to use drones as a mechanism to drop subsea instruments into the water, while others are proposing to use drones that not only go through air, but then dive into the watery depths. Non-aerial Entries include autonomous surface vehicles carrying subsea robots that will return to the ‘mothership’ when their work is done, as well as vehicles and robots that will remain beneath the sea surface from the moment they leave the shore.

The 21 semi-finalist Teams will now have to prove their technology in the field in Round 1 testing, which will take place later this year. As with every XPRIZE, the Shell Ocean Discovery XPRIZE sets audacious goals. In Round 1, the Entries that these Teams are developing will have only 16 hours to map the sea-floor at depths of 2000m and produce a high-resolution map (at least 5m horizontal resolution, at least 0.5m vertical resolution) of at least 20% of the 500km2 Competition Area. Additionally, Teams will have to bring back 5 images of an archeological, biological, or geological feature, as well as an image of an object that we will specify. As if this wasn’t enough, they have an additional hurdle to overcome – they will have to deploy from the shore with no humans allowed in the competition area.

To put this challenge into perspective, it can take days to map 500 km2 of the ocean using current state-of-the-art technologies. If you want 5m accuracy, mapping can take over a week and requires going out on a ship, which can easily cost over $60,000 per day, making it a very expensive endeavor. The cutting-edge technology that will come out of this XPRIZE will truly revolutionize our ability to access the ocean.

In addition to the main Prize of $6 million, a dozen Teams have also opted to compete for the National Oceanic and Atmospheric Administration Bonus Prize of $1 million. These Teams are developing pioneering technology to detect a chemical or biological signal underwater and then autonomously track that signal back to its source.

Partnering with us to truly make this competition a global success are industry titans, Fugro and Esri. Fugro, the industry leader in ocean mapping and survey, will be provide the baseline maps against which the judging panel will assess the Entries. Esri, the global leader in GIS, is providing software to the Teams to produce their maps.

We are looking forward to an exciting competition with exponentially evolving technologies, including smart underwater robots, robot swarms, drones, and artificial intelligence to perform the tasks that have been laid out. These Teams will really take us where no one has gone before! As their journeys of discovery set sail, we invite you to follow the competion and this phenomenal mission to map our beautiful planet.

If you liked this article, you may also be interested in:

• XPRIZE calling all innovators to help map our ocean
• Unmanned and underneath: Stay in the know about underwater drones
• Into the deep: Underwater machines keep an eye on climate change
• What you need to know about underwater drones
• CoCoRo: Tracking the development of the world’s largest autonomous underwater swarm
• Ocean-going robot fleet successfully completes fish-tracking mission

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Image courtesy of FIXO3

The shells on crustaceans and molluscs off the Norwegian Atlantic coast are not as thick as they once were. This is because of ocean acidification, where increased carbon dioxide (CO2) in the atmosphere is absorbed by water, which raises its acidity. Researchers on the EU-funded FixO3 project, which finishes in 2017, are able to keep track of the growing CO2 and rising acidity levels in the water where these creatures live thanks to a device in the Norwegian Sea which collects data round the clock from as deep as 2 000 metres.

Image courtesy of EMSO

Similar tools are being set up all around Europe as part of the European Multidisciplinary Seafloor and water-column Observatory (EMSO), which is part of the EU’s centrally coordinated research infrastructures, to measure how human activities affect our oceans and worsen climate change. As well as providing food and producing oxygen through the organisms that live within them, our oceans and seas regulate our climate by transporting warm water from the equator to the poles and cold water in the other direction. Researchers are using sophisticated machines to track how our oceans are changing, from small cameras that can record high-definition videos of the ocean floor, to others that can detect earthquakes, measure temperature and pressure, and record sounds.

Image courtesy of EMSO

Data is transmitted from machines at the bottom of the ocean either through fibre optic cables or via buoys on the surface, which are linked to satellites. Scientists on land can then track this data, either in real time or with a delay depending on the system, and monitor pollution, climate change and even tsunamis over time. While the machines do require regular maintenance, it means that researchers are able to study the long-term health of our oceans while cutting down on costly expeditions.

Image courtesy of EMSO

Some of the findings to come out of these observatories are expected to be included in future reports by the UN’s influential Intergovernmental Panel on Climate Change. EMSO’s tools, such as one monitoring seismic activity and pressure on hydrothermal vents in the Mid-Atlantic Ridge, will also help the EU know if it has reached its goals of improving the health of Europe’s seas by 2020, as set out in the EU’s Marine Directive. EMSO is also the European counterpart of initiatives in the US, Japan, China, Australia and other countries and will help with international collaboration in the Global Ocean Observing System, a worldwide effort to track changes in our waters.

Image courtesy of EMSO

Similar tools are being set up all around Europe as part of the European Multidisciplinary Seafloor and water-column Observatory (EMSO), which is part of the EU’s centrally coordinated research infrastructures, to measure how human activities affect our oceans and worsen climate change. As well as providing food and producing oxygen through the organisms that live within them, our oceans and seas regulate our climate by transporting warm water from the equator to the poles and cold water in the other direction. Researchers are using sophisticated machines to track how our oceans are changing, from small cameras that can record high-definition videos of the ocean floor, to others that can detect earthquakes, measure temperature and pressure, and record sounds.

This article was first published on Horizon: The EU research and innovation magazine. Click here to view the original article.

If you liked this article, you may also be interested in:

• Unmanned and underneath: Stay in the know about underwater drones
• What you need to know about underwater drones
• Autonomous underwater system for monitoring health of Venice Lagoon demos at #EXPO2015
• CoCoRo: Tracking the development of the world’s largest autonomous underwater swarm
• Ocean-going robot fleet successfully completes fish-tracking mission

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Credit: Robovalley

By: Jurjen Slump

QR-codes that provide autonomous vessels with traffic information on buoys, ships or port buildings. Algorithms for controlling a ship’s unexpected movements. Fleets of autonomous ships that are monitored from on-shore control centres. Researchers at Delft University of Technology are working on the future of shipping. They envision a naval world that will be drastically different from today’s shipping industry.

The future is unmanned. And while everybody is talking about Tesla and Google’s self-driving cars, one tends to forget about what is happening in the oceans. As of now, robotic ships are only sailing in labs, but they will be eventually steaming out into the real world. Rolls Royce predicts that it will test a fully autonomous, unmanned ocean-going ship by 2035. It won’t be long before remote-controlled ships will appear on the high seas, as is shown in this futuristic video from Rolls Royce.

https://www.youtube.com/watch?v=vg0A9Ve7SxE

Autonomous vessels will have a major impact. “Imagine what will happen if you save cost and time on one of the biggest sectors in the world”, says Vittorio Garofano of the faculty of Mechanical, Maritime and Materials Engineering (3mE) at Delft University of Technology. The benefits are clear: cost reduction, reduced emissions, increased safety and increased cargo capacity, to name a few. Besides, people won’t lose their jobs over this development, as by 2025 a shortage of approximately 150,000 maritime officers is expected.

Credit: Robovalley

Adaptive control algorithms
But there are a lot of challenges that must be met before the captain can leave the bridge and the algorithms take control. For example, which technologies are needed so that autonomous ships can function hundreds of kilometres from shore? How can robotic ships be made as safe as conventional ships? How should they interact with each other and how do you make sure that the power and propulsion systems work properly when nobody’s on board?

These are some of the key challenges that the research of Delft University of Technology focusses on. Several algorithms have been developed to ensure a safe and robust operation, explained PhD student Ali Haseltalab during a colloquium on autonomous vessels last week. “With an adaptive control algorithm, which learns the dynamics of the propeller, you can control the ship perfectly”, Haseltalab told. Other algorithms are developed for power management, fault-detection and health monitoring.

The Grey Seabax
These mathematics are put to the test at a 3mE-laboratory, which consists of two giant water tanks with QR-codes on the sides. To study the dynamical behaviour of autonomous vessels, researchers built 3 prototypes: The Grey Seabax, the Amphibian and the Delfa-1. The Grey Seabax is a remote-controlled vessel or RCV – the first essential step towards a fully autonomous ship.

One of the things Garofano and his colleagues are working on is a robust communication infrastructure. The Grey Seabax recognises QR-codes and will be able to respond accordingly. These binary markers can be used for several purposes: they could represent a waypoint, a traffic redirection signal or a building ashore.

Credit: Robovalley

Distributed control
The computer lies at the heart of robotic ships. To enhance safety, it is important that the central computer hands over as much as control to intelligent sensors and actuators. With the Amphibian and Delfia-1, research is being done on the shift from centralised to distributed control. “If something fails, the system continues”, Garofano says. “That increases the survivability.”

With the Amphibian being a hovercraft, new transportation routes can be explored. But the vessel is still very difficult to control, tells Garofano. “GPS offers an accuracy of metres, but we need millimetres.”

Credit: Robovalley

Surveillance and inland shipping
Back to the future. Where will the first robotic ships be deployed? For surveillance, maritime litter collection, patrolling and inspection, says associate professor Rudy Negenborn. “They can be used for monitoring water quality, to make sure that nobody can dump chemicals into the ocean unnoticed.”

Negenborn thinks that the big container shipping corporations will be the last to switch to robotic ships. They will first appear on the rivers. “For inland shipping businesses, the salary costs are relatively high compared to big container ships”, says Negenborn. “For them, autonomous shipping is more interesting.”

Safety
All researchers agree that, in the end, safety will be one of the biggest advantages of robotic ships. Nowadays, at least 70 percent of all incidents at sea are caused by human error. Autonomous shipping will lead to a drastic reduction of the number of incidents and casualties. “Robots will ensure consistency of operations”, explains Haseltalab. “Autonomous systems will always be there. They don’t get tired.”

If you liked this article, you may also be interested in reading more about robotic ships and underwater drones:

• Designing and deploying a fleet of autonomous boats on the canals of Amsterdam
• Who goes where? Simulated robot boats self-organize to defend against attacks
• Robots Podcast #132: The OpenROV project, with Eric Stackpole and David Lang
• CoCoRo: Tracking the development of the world’s largest autonomous underwater swarm
• Unmanned and underneath: Stay in the know about underwater drones

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

US Navy personnel launch an MK18 MOD2 UUV in August 2016. Credit: Mass Communication Specialist 1st Class Blake Midnight/ USN

Editor’s note: This article is adapted from a post from November 2015 and includes recent developments and updated spending figures. 

In October 2016, the U.K. Royal Navy hosted Unmanned Warrior, a massive exercise involving 18 nations that focused on operations involving unmanned air, ground, and maritime vehicles. Popular Mechanics called the exercise the “world’s biggest robot war games.” The Unmanned Warrior exercise offered perhaps the highest profile test to date of an emerging class of unmanned systems: underwater drones, also known as unmanned undersea vehicles (UUV) or autonomous undersea vehicles (AUV). The development of such systems is a major priority for the U.S. Navy and navies around the world. These drones, some of which could eventually be capable of remaining submerged for months at a time, will serve in a variety of roles, including anti-submarine warfare and mine-hunting.

According to the Autonomous Undersea Vehicle Applications Center, a non-profit industry advocacy organization, there are 255 unique configurations of UUVs in service today, including 147 different vehicle platforms. That number is likely to grow in the coming years as the technology improves. Here’s what you need to know about UUVs:

Background

There are two types of unmanned submarines: remotely operated underwater vehicles (ROVs) and unmanned undersea vehicles (UUVs). ROVs are generally tethered vehicles that are piloted by crew members above the surface, while UUVs are not tethered and are designed to operate for longer periods of time and at greater distances from the operator than ROVs. In 1957, the University of Washington’s Applied Physics Laboratory, with support from the Office of Naval Research, developed the Special Purpose Underwater Research Vehicle (SPURV). One of the first UUVs, the SPURV could dive up to 10,000 feet and operate for four hours. By the time the SPURV program ended in 1979, other ROVs and UUVs were being developed in the United States, particularly for marine exploration and research. In 1985, the Argo, a remotely operated underwater vehicle, found the wreckage of the Titanic and, a World War II battleship called the Bismarck in 1989.

A 1983 article in Popular Science detailing the use of UUVs for scientific exploration. Via AUVAC.

After the Soviet Union mistakenly shot down Korean Airlines 007 in 1983, the U.S. Navy deployed the Deep Drone—a remotely operated vehicle built by Eastport International to aid in the search for the “black box” of the passenger plane. In 1999, the Deep Drone was used again, successfully locating the flight recorders of EgyptAir 990 off the coast of Nantucket Island. In 2011, a team from Woods Hole Oceanographic Institute used UUVs to help find the wreckage of Air France 447. Underwater drones have been repeatedly sent to aid the search for Malaysian Airlines 370 (most recently in October 2016 when a Chinese vessel was outfitted with a remotely operated vehicle for deep sea exploration).

The unmanned submersible Deep Drone is ready for deployment from the fleet tug, USNS NARRAGANSETT (T-ATF 167) during search operations for Korean Airlines Flight 007. All Hands Magazine – July 1984.

A technician controls the movement of the unmanned submersible Deep Drone from a control panel in the fleet tug USNS NARRAGANSETT (T-ATF 167). The submersible is being used to aid in the search for Korean Air Lines Flight 007. Photo from July 1984 All Hands Magazine.

As a September 2015 article in the New York Times pointed out, due to the lower operating costs and the ability to stay underwater for extended periods of time, ROVs remain an extremely attractive option for deep sea researchers. In recent years, a new generation of underwater drones have allowed both universities and small businesses access to new opportunities for undersea exploration in the same way that unmanned aerial vehicles have democratized access to the sky. Startup companies like Blue Robotics and OpenROV have utilized off-the-shelf hardware to create ROVs that are a fraction of the cost of those used in locating the Titanic and the Bismarck. In July 2016, an OpenROV team used one of their underwater drones to get a closer look at steamship Tahoe, an early 20th-century wreck in Lake Tahoe.

In the 1990’s, after two U.S. Navy ships were badly damaged by Iraqi sea mines in the Persian Gulf during Operation Desert Storm, the Navy began investing heavily in Mine Countermeasures and initiated a program to build UUVs that could disable enemy sea mines. The Navy’s 1994 Unmanned Undersea Vehicle (UUV) Plan set the priorities for the program; topping the list was the need to develop UUVs for organic and clandestine mine detection. In other words, the Navy needed a system that could avoid detection by operating underwater in advance of dedicated minesweepers on the sea surface. In 1996, Northrop Grumman developed the Near-Term Mine Reconnaissance System, a two-vehicle platform that was designed to be launched from the torpedo tube of a submarine and connected to the vessel by a fiber-optic tether. The NMRS was replaced by the Long-Term Mine Reconnaissance System. The LMRS program produced the 20 ft.-long Boeing AN/BLQ-11 drone.

The UUVs of Today

In the years since the Navy’s first serious foray into UUVs with the NMRS and LMRS, a number of underwater drones with varying capabilities have emerged. With the release of the Unmanned Undersea Vehicle Master Plan in 2004, the Navy established nine priorities for future UUV capabilities, ranging from anti-submarine warfare to delivery supply and even strike missions. Among the most prolific unmanned undersea drones are the Remote Environmental Monitoring UnitS (REMUS) family of UUVs. The REMUS product line was developed by Woods Hole Oceanographic Institution and Hydroid, a Massachusetts-based UUV manufacturer that is now owned by Kongsberg Maritime. During Operation Iraqi Freedom in 2003, a REMUS 100 UUV was used to help clear sea mines from the waterways around the port of Umm Qasr, marking the first time a UUV was deployed in a combat environment. The larger, more capable REMUS 600 is a torpedo-shaped underwater drone designed to carry a large payload of sensors down to a depth of 600 meters for meteorological and oceanographic surveying. The Kingfish, a U.S. Navy variant of the REMUS 600, is expected to replace the U.S. Navy’s Marine Mammal Program, an effort to use dolphins and sea lions to detect sea mines, within a few years.

A screenshot of a slide from a presentation by Rear Adm. Tim Gallaudet, Oceanographer and Navigator of the Navy on October 25, 2016.

Mine countermeasures (MCM) remains the top priority for the Navy’s UUV programs. The Navy traditionally fields an array of air and sea systems to counter enemy mines, including fleets of minesweepers and low-flying helicopters that use sonar to detect mines. The Navy is investing in maritime unmanned systems to replace the manned vehicles used for the MCM mission. It has decided to couple unmanned mine countermeasures to existing manned vessels, including to the new Littoral Combat Ship (LCS), creating the MCM “module” for the LCS. Instead of sending out human divers, UUVs can be used to disable the devices with a small explosive charge, eliminating the risk to sailors. UUVs are also fitted with sonar and other sensors that can autonomously map out minefields. The Navy is under pressure to develop and deploy UUVs, as existing platforms like the Avenger-class minesweeper and the Sea Dragon helicopters are quickly reaching the point of retirement.

In Fiscal Year 2017, the Department of Defense is anticipated to spend at least $350 million on underwater drone procurement and research. Most of this funding is concentrated in Navy programs, with additional funds for UUV research allocated by the Defense Advanced Research Projects Agency and the Office of the Secretary of Defense. The Navy will transition some UUV projects from the research to the procurement stage, marking a significant development in the plan to acquire UUVs. Since some UUV research projects are packaged within larger line items, however, the total amount the DoD will spend on underwater drones is likely more than $360 million. For example, a large portion of the Navy’s $77.2 million research project into anti-submarine warfare surveillance technologies will go toward underwater drone technology.

The Navy’s 2017 UUV procurement efforts are divided into packages like mine countermeasures and meteorological equipment. For the underwater explosive ordnance disposal package, the Navy intends to spend a total of $21 million on a few different types of unmanned undersea vehicles: the Hydroid MK18 Mod 1 Swordfish, MK18 Mod 2 Kingfish, and Bluefin Robotics HULS ROV. Under the program to deliver a replacement for manned minesweepers, the Navy will purchase two General Dynamics Knifefish UUVs for a total of $20.5 million. Finally, under the Meteorological Equipment program, the Navy will purchase 20 Littoral Battlespace Gliders from Teledyne Brown Engineering and four Autonomous Undersea Vehicles from Hydroid for a total of $22.45 million.

The mine countermeasures package for the Littoral Combat Ship has been one point of contention in recent years between the Navy and Congress. In Fiscal Year 2017, the Navy will purchase one General Dynamics Knifefish UUV for mine countermeasures for the Littoral Combat Ship. It will not, however, devote funds for the Remote Minehunting System, which was officially cancelled by the Navy in March 2016. The main product of the RMS was Lockheed Martin’s Remote Multi-Mission Vehicle (RMMV), an autonomous UUV that was intended to be deployed from a Navy ship to help search for mines by towing a sonar. The concept for the RMS was actually developed in the late 1990s, and it has had a checkered history. According to the latest Selected Acquisition Report for the RMS, the acquisition cost of a single system has more than doubled from $13 million to $27.7 million between 2007 and 2015.

In September 2015, Senators John McCain and Jack Reed criticized the Navy’s Remote Minehunting System program, arguing that the RMMV had failed to meet requirements and could be scrapped altogether. “Put simply, while estimated overall spending on RMS is only a little higher than originally planned, it is only yielding half the number of systems, at more than double the unit cost, and it is taking twice as long to field it,” Senator McCain wrote in a review of the RMS program. “Also, it doesn’t work,” he added. Although RMS is no more, the AN/AQS-20A advanced minehunting sonar that was developed for the RMMV will be funded in Fiscal Year 2017.

The Next Generation of Underwater Drones

Defense funding for research into UUVs is around three times more than funds for procurement. Some of these research programs are dedicated to UUVs that the Navy is currently purchasing like the General Dynamics Knifefish UUV, which is slotted to receive $11.6 million in research funding in FY17. However, the Navy and other DoD departments have allocated considerably more funds toward researching the next generation of UUV systems and technologies. The largest of these research programs are for the Large Displacement Unmanned Undersea Vehicle (LDUUV) and the Extra Large UUV (XLUUV). Unlike UUVs intended for a specific mission such as detecting sea mines, these larger UUVs are designed to have multiple mission types, including persistent intelligence gathering capabilities and payload delivery.

The Boeing Echo Voyager could be one of the XLUUV designs that the Navy pursues in the future. Credit: Boeing

The essential difference between the LDUUV and XLUUV has to do with the fact that LDUUVs, like existing UUVs, are intended to be deployed from Navy ships, while the XLUUV could be deployed from a Navy base and operate independently of a host vessel. The LDUUV-class UUVs will have a hull diameter of more than 36 inches, while the XLUUV will have a maximum diameter of 54 inches. (The majority of UUVs generally have a hull diameter of between 7 and 21 inches.) The LDUUV will have an endurance measured in weeks, while the XLUUV will be deployed for long-range missions, traveling for months at a time and up to 2,000 nautical miles, according to a September 2016 request for information. In anticipation of the Navy’s demand for large UUVs, some companies are already independently investing research dollars into developing the next generation of underwater drones. In March 2016, Boeing unveiled the Echo Voyager, a massive 51-foot-long UUV that could potentially operate autonomously for months at a time.

In Fiscal Year 2017, the Navy plans to invest $67.6 million in researching LDUUVs and $78.6 million in a new research program dedicated to developing the XLUUV and associated technologies. The Navy has proposed purchasing five demonstrator XLUUVs in Fiscal Year 2017. However, this number could be reduced by Congress in the Fiscal Year 2017 National Defense Authorization Act. In the Senate’s NDAA report from earlier this year, it took issue with this proposal, suggesting that the cut the research program by $34.4 million by only purchasing two XLUUVs.

Beyond developing larger UUVs, one important area of focus is on improving the ability of unmanned systems to communicate and operate between air, surface, and undersea domains. For example, just as manned ships and helicopters currently collaborate to locate and destroy sea mines, unmanned helicopters like the MQ-8 Fire Scout could work with unmanned surface and undersea vehicles to accomplish the same mission while keeping human operators out of harm’s way. At the Annual Naval Technology Exercise in August 2016, several U.S. defense firms demonstrated how unmanned systems could work collaboratively between the air, surface, and undersea domains. An AeroVironment Blackwing UAV communicated with a swarm of unmanned undersea vehicles and Lockheed Martin successfully launched a UAV from a Marlin MK2 autonomous underwater vehicle. Northrop Grumman paired UUVs with unmanned surface vehicles and a Fire Scout UAV.

The Department of Defense has allocated funding for more experimental UUV programs, sometimes within larger line items. In Fiscal Year 2017, the Defense Advanced Research Project Agency has allocated $24.2 million towards the Hydra, project to develop a network of unmanned underwater assets that could be deployed quickly in response to emerging threats. Another project, the Upwards Falling Payloads program, is slotted to receive $14 million in FY17. The goal of this initiative is to design affordable unmanned systems that, when remotely activated, can rise to the surface to deliver either a waterborne or airborne payload of explosives or supplies. The Office of the Secretary of Defense also funds UUV research projects, although on a smaller scale than the Navy and DARPA. OSD’s Undersea Warfare and Technology Focus Area, for example, will receive $7.5 million for general research into UUV issues such as command and control and communications.

Future of the Field

Recent advances in UUV technology and affordability and geostrategic interests are among the factors driving interest in underwater drones at home and abroad. Other countries are pursuing UUV programs in the race to develop and field unmanned systems.  In 2014, the Yomiuri Shimbun reported that Japan and the U.S. were collaborating on a 33-foot-long UUV that would be used to autonomously patrol areas using sonar. A RAND report on Chinese drones released earlier this year found that Beijing is funding at least 15 different university research programs into unmanned undersea and surface vehicles with particular emphasis on UUV projects. A Russian news broadcast in November 2015 confirmed suspicions that Russia was building a long-range UUV capable of carrying a nuclear warhead. In October 2016, the U.K. and France announced that they had awarded a $164 million contract to a group of European defense firms to develop a UUV for mine countermeasures.

The Russian slide showing a concept for a nuclear-armed UUV.

Unmanned submersibles could provide a low-cost alternative to manned platforms for missions that are particularly dangerous, like minesweeping, or for mission that are tedious, like patrolling. UUVs, particularly those envisioned by DARPA, could also be a way to assert military presence in an area that is difficult to reach or geopolitically contentious. Like the minesweeper concepts, UUV platforms could be built into the capabilities of existing manned platforms like the Littoral Combat Ship or submarines. Alternatively, larger UUVs may be deployed from ports and conduct missions independent from manned ships.

However, as a 2009 RAND study of UUV systems pointed out, some of the Navy’s proposed UUV missions were not practicable and remain out of reach. Issues with reliability, specifically within the realms of power systems and underwater communications, suggested to the RAND researchers that the technology was not quite mature enough. The controversy over the Remote Minehunting System is one example of how the Navy has struggled to develop an adequate countermine alternative to minesweepers and to successfully sell UUV technology to lawmakers on Capitol Hill. While UUVs present significant advantages in terms of cost and safety over manned platforms, it will be some years before UUVs are accepted into the fleet.

For updates, news, and commentary, follow us on Twitter.

This post originally appeared on the Center for the Study of the Drone at Bard College.

If you liked this article, you may also be interested in:

• What you need to know about underwater drones
• Autonomous underwater system for monitoring health of Venice Lagoon demos at #EXPO2015
• CoCoRo: Tracking the development of the world’s largest autonomous underwater swarm
• Ocean-going robot fleet successfully completes fish-tracking mission
•  ROV operations expenditures to grow to $1.5 billion by 2017

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

There is a very interesting planet near us. It has almost 130 million square miles of land and the longest continuous mountain chain in the solar system, spanning over 40,000 miles. This planet receives very little sunlight, so temperatures hover around 40C (39oF) regardless of the time of year, and atmospheric pressure is, on average, a crushing 400 times greater than our own. Our advanced probes tell us that there is very little oxygen and the environment is corrosive, so it is difficult to imagine anything living under these conditions. And yet we know with 100% certainty that life exists on this planet. Is this place real or science fiction? It’s real—and it’s only 4km away!

This is the planet at the bottom of our ocean.

Although it is so close to us, the deep ocean is a place we know very little about. We have better maps of Mars, hundreds of millions of miles away, than we do of our own sea floor. We don’t know how many volcanoes or mountains are hidden in the ocean, let alone what strange life forms exist down there. Less than 5% of the ocean has been explored because it is opaque and a physically challenging environment. It is also expensive to access; ships may cost over $60,000 per day to operate and vehicles that can go down into the deep sea can cost over $1 million. We need new breakthrough technologies that can help us to overcome the costs and can operate quickly and easily under great pressure in these cold and corrosive watery environments.

The Shell Ocean Discovery XPRIZE is a $7 million competition challenging teams from around the world to build advanced deep-sea underwater robots that will provide safe access to this tough environment, paving the way for autonomous, fast, and high-resolution ocean exploration. The success of this prize will result in technologies with which we can fully explore and map the ocean floor, uncovering our planet’s greatest wonder and allowing us to sustain and protect our deep-sea resources. We are also asking teams to advance our ability to see what is down there by producing high-resolution images of biological, geological, and archeological features. Embedded in this competition is a $1 million bonus prize from the National Oceanic and Atmospheric Administration (NOAA) to incentivize the development of smart technologies that can detect underwater chemical or biological signals and trace them to their source.

These breakthrough technologies will assist with search and rescue operations, giving us the ability to quickly and easily find planes and vessels that are lost at sea. The small fraction of the ocean we have explored has already provided us with potential treatments for cancer, Alzheimer’s, and AIDS. Imagine what other cures are down there. Underwater archeologists have uncovered long lost treasures from ancient shipwrecks such as the San Jose galleon off the coast of Columbia, shining a light on human history (it is estimated that there are 3 million shipwrecks at the bottom of the ocean). Our limited underwater images have shown astonishing creatures with abilities to camouflage themselves, generate their own electricity, and glow in the dark. What else might we discover if we could explore more of this vast resource?

By increasing our access to the ocean, we will increase our understanding of this wonderful and mysterious world, and by doing so we will realize it’s true value. The ocean is responsible for 50% of the oxygen we breathe, is the primary driver of our weather, and is a source of food for over 2 billion people. As such, it is a precious resource—and we need to make sure it remains healthy. The advances sparked by the Shell Ocean Discovery XPRIZE will also provide access and transparency in the deep-sea for better management and conservation efforts.

Join us on this grand adventure. We are calling all innovators to help us discover this new planet. Form a team and sign up to compete, spread the word, become a resource for teams, or follow us and find out about the mysteries of the deep at OceanDiscovery.xprize.org. Together, we can usher in a new and unprecedented age of ocean exploration.

Our penultimate video features the initial “Big Vision” trailer we produced at the beginning of this project. The video showcases the basic components of the robotic system we targeted (surface station, relay chain, ground swarm) and how we imagined our collective of underwater robots forming coherent swarms.

Our “Big Vision” helped us stay focussed on the project, ensuring that all project partners shared the same goal for their work. We based the video on a small simulator, written by us. Little did we know that this simple simulator would develop into a critical tool that would eventually include underwater physics with fluid dynamics for simulating underwater swarms. The simulator was also used in evolutionary computation to find good shoaling behavior for our robots.

Next week we will be publishing our final video in the Year of the CoCoRo. Our finale will show the whole system on real underwater robots in a comparable setting and how our initial vision finally became reality!

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we’ll be uploading a new weekly video detailing the latest stage in its development.

As part of the total $7M prize purse, the National Oceanic and Atmospheric Administration (NOAA) is offering a $1M bonus prize to teams that demonstrate their technology can “sniff out” a specified object in the ocean through biological and chemical signals. David Schewitz, Shell vice president of geophysics for the Americas, and Richard Spinrad, chief scientist at NOAA, joined Diamandis on stage to launch the new competition.

“Our oceans cover two-thirds of our planet’s surface and are a crucial global source of food, energy, economic security, and even the air we breathe, yet 95 percent of the deep sea remains a mystery to us,” Diamandis said. “In fact, we have better maps of the surface of Mars than we do of our own seafloor. The Shell Ocean Discovery XPRIZE will address a critical ocean challenge by accelerating innovation to further explore one of our greatest unexplored frontiers.”

The three-year competition includes nine months for team registration, 12 months for initial solution development and 18 months to complete two rounds of testing and judging by an expert panel. In each round, teams will complete a series of tasks, including making a bathymetric map (a map of the sea floor), producing high-resolution images of a specific object, and identifying archeological, biological or geological features. Teams also must show resiliency and durability by proving they can operate their technologies, deployed from the shore or air, at a depth of up to 4,000 meters.

“Spurring innovation and creating radical breakthroughs in ocean discovery are what excite us about collaborating with XPRIZE,” Schewitz said. “Shell recognizes the need to leverage the full power of innovation: the capacity for doing things differently and better than before.”

A $4M Grand Prize and $1M Second Place Prize will be awarded to the two teams that receive the top scores for demonstrating the highest resolution seafloor mapping, after meeting all minimum requirements for speed, autonomy and depth. Up to 10 teams that proceed to Round 2 will split a $1M milestone prize purse. And the $1M NOAA bonus prize will be awarded to the team that can trace a chemical or biological signal to its source.

“The goal of the $1M NOAA bonus prize is to identify technology that can aid in detecting sources of pollution, enable rapid response to leaks and spills, identify hydrothermal vents and methane seeps, as well as track marine life for scientific research and conservation efforts,” said Spinrad.

The Shell Ocean Discovery XPRIZE is part of the 10-year XPRIZE Ocean Initiative – a commitment made to launch five multi-million dollar prizes by 2020 to address critical ocean challenges and make the oceans healthy, valued and understood. XPRIZE awarded the Wendy Schmidt Oil Cleanup XCHALLENGE in 2011 and the Wendy Schmidt Ocean Health XPRIZE in July 2015.

For more information, and to register your intent to compete, visit oceandiscovery.xprize.org.

About XPRIZE
Founded in 1995, XPRIZE is the leading organization solving the world’s Grand Challenges by creating and managing large-scale, high-profile, incentivized prizes in five areas: Learning; Exploration; Energy & Environment; Global Development; and Life Sciences. Active prizes include the $30M Google Lunar XPRIZE, the $20M NRG Cosia Carbon XPRIZE, the $15M Global Learning XPRIZE, the $10M Qualcomm Tricorder XPRIZE, and the $7M Adult Literacy XPRIZE. For more information, visit www.xprize.org.

About Royal Dutch Shell
Shell has been a technology pioneer for more than 100 years, and has come up with many industry-transforming “firsts” to deliver energy its customers and partners need. Since 2007, Shell has spent more than $1 billion annually on research and development. In 2014, our research and development expenditures were $1.2 billion. Shell’s technical and engineering staff amount to more than 43,000.

Shell companies have operations in more than 70 countries and territories with businesses including oil and gas exploration and production; production and marketing of liquefied natural gas and gas to liquids; manufacturing, marketing and shipping of oil products and chemicals and renewable energy projects. Royal Dutch Shell plc is incorporated in England and Wales, has its headquarters in The Hague and is listed on the London, Amsterdam, and New York stock exchanges. For further information, visit www.shell.com.

About National Oceanic and Atmospheric Administration (NOAA)
The National Oceanic and Atmospheric Administration (NOAA) is a science-based federal agency within the Department of Commerce with regulatory, operational and information service responsibilities with a presence in every state and U.S. territories. NOAA’s mission is to understand and predict changes in the Earth’s environment, from the depths of the ocean to the surface of the sun, and to conserve and manage coastal and marine resources. For more information, visit www.noaa.gov.

CoCoRo’s humble beginnings.

Our underwater swarm research started in a few cubic centimeters of water with some naked electronics on a table. Over the next three and a half years, our swarm increased by a factor of 40, and the size of our test waters increased by a factor of 40 million as we went from aquariums and pools, to ponds, rivers and lakes, and finally ending up in the salt water basin of the Livorno harbour. Quite a stretch for a small project!

Our new project, subCULTron, which extends the work of CoCoRo, will scale up the swarm size to 120+ robots, and will take place in an even larger body of water: the Venice Lagoon.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we’ll be uploading a new weekly video detailing the latest stage in its development. 

During the past year we have shown many swarm algorithms in various experiments. The spotlight was always on the Lily and the Jeff robots. However, there is now another star in the team and this trailer is dedicated to this special agent: the base station!

The base station was finished towards the end of the project, thus, we had to develop (i.e. hack) many surrogates and placeholders for it over the course of the project. We got so experienced with it that we could quickly hack a surrogate base station from almost anything that was lying around in the lab: styrofoam, cans, boxes … whatever was around and handy. This video shows some of those creations.

A few months before the final review we had the real thing ready: a typical Italian machine (like Italian cars) made by our partners from SSSA (Pontedera). It was fast as hell, highly manoeuvrable, and elegant. The base station has a docking device and can actively manoeuvre, dock and undock robots and carry three attached spare robots with it. With this central masterpiece, we were ready for our final review.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we’ll be uploading a new weekly video detailing the latest stage in its development.

Most of the videos from The Year of CoCoRo were shot during workshops we held throughout the project. These workshops, which were usually focussed on one or several specific demonstrators, were what drove our international team of collaborators to implement mechanical hardware, electronics and software into working installations. This form of workshop-driven development proved to be very successful, and by the end of the project we were able to show 17 working final demonstrators that show the versatility of robot swarms. 

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we’ll be uploading a new weekly video detailing the latest stage in its development.

Transcript below.

In this episode, Ron Vanderkley speaks with Stefan Williams of the University of Sydney’s Australian Centre for Field Robotics, Marine Systems Group. They discuss the future of Autonomous Underwater Vehicles (AUVs), and a recent expedition where they used multi-session SLAM to map the famous Antikythera Shipwreck (circa 60-80 B.C.), one of the richest ancient wrecks ever discovered. It is located under 55m of water on the NE coast of the island of Antikythera. The site is famous for the first Analog Computer known as the Antikythera Mechanism, a geared device designed to calculate and display celestial information, including phases of the sun and a luni-solar calendar.

The ACFR leads Australia’s Integrated Marine Observing System (IMOS) AUV Facility. IMOS is a nationally coordinated program designed to establish and maintain the research infrastructure required to support Australia’s marine science research. The IMOS AUV facility generates physical and biological observations of benthic variables that cannot be cost-effectively obtained by other means.

Stefan Williams
Stefan Williams is part of the Faculty of Engineering & Information Technologies, School of Aerospace, Mechanical and Mechatronic Engineering, University of Sydney’s Australian Centre for Field Robotics Marine Systems Group.

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Transcript

Ron Vanderkley: Stefan, can I get you to introduce yourself to our podcast listeners?

Stefan Williams: I’m a professor of Marine Robotics at the University of Sydney’s Australian Center for Field Robotics. We undertake both fundamental and applied research in the area of marine robotics and the development of underwater systems.

Ron Vanderkley: What is the main goal of the Marine Group?

Stefan Williams: Our focus over the last decade or so has been to develop tools and techniques for better understanding marine environments. We work in a variety of different application areas, from ecology to archeology and geoscience, and we work in the engineering research areas that underpin these applications – such as vehicle navigation, control planning, machine learning, and computer vision – in order to provide better understanding and information to our end users about the data we are collecting.

Ron Vanderkley: When you talk about autonomous marine surveying systems, what kind of capabilities are we talking about today? Is this a mix of manual and autonomous techniques? Where are the AUVs?

Stefan Williams: The main work that we do is on autonomous underwater vehicles. These are vehicles that are disconnected from the surface. We program them with a particular mission that we’d like them to execute, which might be a big long transect or a particular depth profile, or it might include very detailed studies over a particular feature of interest. Then a vehicle goes down and executes that mission. We typically stay in communication with the vehicle using acoustic modems; the vehicle tells us what it’s doing, it gives us feedback on the data that it’s collecting, and we have some supervisory control to tell it “no, you’re in the wrong spot, please come up” or “there’s some issue with the mission so abort” for example. We also have some ability to redirect the missions while they’re underway.

The data is collected onboard the vehicles. Once we recover them we download the data and build detailed 3D models of these marine habitats primarily using vision data, but we also collect multi-beam sonar, so we have acoustic data as well if we’re looking to map broader areas of the sea floor. That’s the core technology that has been developed around these autonomous platforms, but we’ve also built systems for both shallow water surveys using divers, and also very deep-water exploration using remotely operated vehicles. We also have platforms and sensor packages that we can use in other ways beyond just the autonomous platforms.

Ron Vanderkley: You spoke about the acoustic modems. What level of data can you get out of that? Is it to the level of telemetrics? Can you actually sift some data instead of just downloading it at the end of the mission?

Stefan Williams: Most of the data that we get back at the moment is supervisory in nature. The robot will tell us where it is, how deep it is, how far it is off the bottom, and the amount of data or number of images that it has collected. This lets us monitor the progress of the mission and make sure that things are on track. That modem is also piggybacked on positioning information, so it tells us where the vehicle is as an independent measurement from the ship. We then send both the ship position and the observations of the robot’s position relative to the ship down to the vehicle, and it updates its estimate of where it is using that information. We’re able to correct any drift in the navigation estimate using those external observations.

We’ve just taken on a new positioning and modem package in the past year or so that is capable of higher baud rates. In theory, we can get thumbnails of images. I’ve seen some work where they’re actually using this same sort of acoustic modem to do low-bandwidth video across an acoustic length, but that’s a capability we still have to develop.

Ron Vanderkley: I believe that your group is working on some novel enhancement to acoustic communication from the operator to the robot. Is that to increase the baud rate?

Stefan Williams: We’ve just been awarded an Australian Research Council Linkage Grant, which encourages the research community to engage with interested partners. This is a partnership with Tellus Australia – they do a variety of different developments in the marine space, particularly focused on defense applications. The project is really about managing the communication channels: it’s less about developing new acoustic communications equipment than it is about what information is best to send across the acoustic communication channel so that the operator can most effectively monitor the progress of the mission. In an information theoretic sense, we want to decide what information is best from the point of view of giving the operator an idea of what’s happening, and then allowing that operator to make decisions and query for further information from the vehicle. For instance, it might be used to refine and estimate where particular targets are in an environment, or to request more detailed high-resolution components of a particular area that might be of interest to the operator.

Ron Vanderkley: I also was aware of a multi-session SLAM-based AUV mapping system, and there was an example of a wreck that you worked on. What did that entail?

Stefan Williams: We’ve been involved over the past year in projects together with the Woods Hole Oceanographic Institute and the Hellenic Ministry of Culture and Sport (and a few other organizations) in mapping and excavating a 1st century BC shipwreck off the coast of the Greek island of Antikythera. This wreck was first discovered in 1900 and excavated around that time, yielding a whole trove of statues and other ancient artifacts – including something called the Antikythera Mechanism, which is one of the first examples of what people consider to be an analog computer. It was a geared mechanism that was used for predicting celestial motion over a fairly extended period of time to predict where the sun was and the phases of the moon, etc. It’s a really unique artifact that hasn’t been documented previously. It’s a pretty fascinating site to be exploring.

The objective of our work there was to provide the archeologists with a baseline map before they started further excavation on the site. We first visited the site in September 2014 and conducted surveys over the main target area – as well as looking at other targets of interest that had been identified using acoustic multi-beam data collected in the previous year.

We returned in June 2015 and mapped a very extensive part of the coastline around the wreck site because some of the diving operations they’d conducted in 2014 identified other areas of interest. We delivered a map of their target excavation sites and they were able to use those maps in situ. They actually had copies of the maps on iPads, and were able to annotate these while diving underwater in depths of 50-60 meters. They were able to log all of their finds and situate themselves on the wreck site relative to the maps that we generated. It provided them with a baseline of what the site looked like before they started with the excavation operations.

They’ve just finished their field research and an exhibit has opened in Basel, Switzerland showcasing some of the finds and describing the site and some of the archeological work that’s been going on there. It’s been a really exciting project to be part of and has attracted a lot of interest. Our part of that was using AUV systems to give them baseline high-resolution 3D maps of the wreck site.

Ron Vanderkley: It kind of sounds like “Jacques Cousteau meets robot”

Stefan Williams: In fact Jacques Cousteau visited the site in 1976 and they did some excavation that was part of their Calypso program. They did a lot of underwater exploration and recovered a few artifacts, but this is really the first time that the site has been systematically mapped.
Ron Vanderkley: What is the future of AUVs? Where are the challenges?

Stefan Williams: AUVs have a big role to play in expanding our understanding particularly in deepwater environments. Our main focus has been in coastal environments linked to ecological and biological studies. I think there is a role to play also in understanding how changes in oceanography (for example temperature, salinity, and current profiles) are affecting the marine habitat. We have a big program as part of Australia’s Integrated Marine Observing System Program, running AUVs to look at how key ecological environments around the country are changing over time. We revisit these areas, and work closely with biologists and ecologists to document changes.

Another big role for these autonomous platforms is in understanding deepwater environments – getting them out into the abyssal plains to find out more about what’s down in the deep parts of the oceans, of which we know very little about.

One thing that really excites me is looking at some of the advancements that were made in long-term autonomous deployment. A lot of these autonomous underwater vehicle systems are really starting to come into the norm. There’s some fantastic work going on around the world in designing and building these systems both on manned surface vessels that are capable of very long range operation, and on underwater vehicles that are capable of thousands of kilometers of deployment.

That really changes the dynamics of how ocean science can be conducted. It reduces the requirement of having people on very expensive ships out at sea for long periods of time if you can send these robotic systems off to work autonomously. We’ve seen the precursors in oceanography, with autonomous float systems being used to help oceanographers get a better understanding of ocean circulation and physical oceanography on a global scale. The next revolution will be autonomous platforms that can do mapping and excavation work over long periods of time in deep water across the world. I think there’s real potential in that area.

Ron Vanderkley: What is Australia’s role in that? Are we able to create our own submersibles? Are we concentrating on the programming in that arena? Where do you see us?
Stefan Williams: We have covered both areas natively in Australia. Some of these vehicles are being purpose-designed for particular tasks. We’re currently designing our next generation of hydrolution survey vehicle for supporting the Marine Robotics program, and there are programs in Queensland and Tasmania looking at requirements for working under ice or in complex reef environments.

There is work here in Australia in building these sorts of systems, though there is not a very large commercial scene yet (a lot of the commercial platforms are being developed in the US and Europe). But there’s certainly a lot of research effort focused on designing vehicles that are fit for purpose, and I think we’re particularly well known for the mapping and the navigation work that we do. We have links with all the major oceanographic groups around the world. I think Australia as a whole is very well regarded in robotics and autonomous systems – both within the marine space, and also in ground and air vehicles – and is having a big impact in this area.

Ron Vanderkley: Finally, I’d like to ask you about employment prospects in Australia. If you were a student going to university looking at the options, where would you point them?

Stefan Williams: We have a lot of students come both through undergraduate and postgraduate teaching and research schemes, and the majority of them do find work both domestically and internationally. There’s certainly a lot of activity currently in the robotics space. Some of the big companies like Google, Apple and Uber have made well documented forays into autonomous driving systems, and they’re certainly employing a lot of PhD students, post docs and research fellows who have been employed in various labs around the world, so there’s a lot of potential there. We have former students and staff embedded in the majority of those companies.

Here in Australia we have a domestic market with a number of small startups that are really doing well in developing robotics systems and sensor payloads. The experience students get – and the type of skills that they acquire through their study of mechatronics and robotics – have application across a wide variety of domains, from traditional engineering work, to software, electronics and mechanical design work … all the way through finance and business studies. There’s a lot of interest in data analytics and machine learning, and a lot of the tools and techniques that we apply in robotics can be transferred across a variety of different domains.

One of my most recent students is now heading up a small Machine Learning group at one of the major banks. The skills and underpinning tools required here in our lab to classify marine imagery have direct application into other areas. When students ask me about what jobs are out there for mechatronics engineers, I tell them that there are always good jobs for good students. One of the keys to that is really being passionate about the things that you’re studying, and I think robotics excites a lot of people.

I teach an experimental robotics course – it’s an elective in the 4th year mechatronics programs here at Sydney University. The things that the students do in the course of the 13-week curriculum are astounding. They put a lot of time, effort, and thought into building these robotic systems. I think they feel very rewarded at the end, and the feedback I’ve gotten has always been very positive.
Ron Vanderkley: Thank you, Stefan, for your time. I hope to speak to you again soon.

Stefan Williams: It’s a real pleasure, Ron. Thanks for helping to promote the activities of robotics here in Australia and overseas.

This audio interview was transcribed and edited for clarity. 

In March 2014, we exhibited CoCoRo in Hannover, Germany at the CEBIT — Europe’s largest consumer electronics fair. At first we thought we might be out of place and that our exhibit would be overshadowed by the latest flatscreen TVs, smartphones and gaming consoles. We were very wrong: though we had the smallest booth, we were overrun with thousands of people throughout the week, and television and radio crews also stopped by for interviews. By our own estimates, we may just have had the highest rate of visitors per square meter in the whole fair.

It’s not easy to bring a swarm of underwater robots and run live experiments at a consumer electronic show, but we gained a lot of motivation from the public interest we felt there. Thanks to our enduring team members and also to all the people who visited us!

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we’ll be uploading a new weekly video detailing the latest stage in its development.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we’ll be uploading a new weekly video detailing the latest stage in its development. This week’s video shows an autonomous swarm of underwater robots coordinating their motion to form coherent shoals.

The body shape of Jeff robots is much closer to that of a fish than the Lily robots are. With their slim bodies, Jeff robots can tightly flock together and move in one direction as a group. We implemented a simple blue-light-LED-based algorithm that allows neighboring robots to align to each other. This doesn’t work 100% of the time, but it still works quite often. And when we filmed the little fish that observed our experiments with robots in Livorno harbor (see at beginning of the movie), we observed that the natural fish also did not align 100% of the time. In other words, we came pretty close.

We implemented this code in a very short period of time (hours!) towards the end of the project. With more time and more local neighbor communication, the shoaling can be much improved in future. We hope to be able to further extend this in our follow up project, subCULTron.

A UUV is removed from the water during operations off the coast of Bahrain in 2013. Credit: Specialist 2nd Class Michael Scichilone/US Navy/Released

Late last month, Secretary of the Navy Ray Mabus appointed Brig. Gen. Frank Kelley to become the first deputy assistant secretary of the Navy for unmanned systems, a position that was created in April. “The change to the organization is a reflection of the priority we’re placing on this emerging capability,” Secretary Mabus said in a speech at the Unmanned Systems Defense 2015 conference on October 27. Kelley will be responsible for a variety of programs ranging from the high-flying Triton surveillance drone to experimental projects like the Navy’s fleet of swarming patrol boat drones. The new deputy secretary will also be overseeing drones that plumb the depths of the oceans. The development of torpedo-like submarine drones, also known as unmanned undersea vehicles (UUV) or autonomous undersea vehicles (AUV), is a major priority for the Navy. These drones, some of which could potentially remain submerged for months at a time, will serve in a variety of roles including surveillance and mine-hunting. At the Unmanned Systems Defense 2015 conference, Secretary Mabus said that the Navy is aiming to launch a squadron of autonomous UUVs by 2020 that can operate independently from manned vessels.

According to the Autonomous Undersea Vehicle Applications Center, a non-profit industry advocacy organization, there are 251 unique configurations of UUVs in service today, including 144 different vehicle platforms. That number is likely to grow in the coming years as the technology improves. Here’s what you need to know about UUVs:

Background

Submarine drones are generally classified either as remotely operated underwater vehicles (ROVs) or as UUVs. The former is a tethered vehicle that is piloted by crew members aboard a nearby vessel, while the latter is not tethered and is designed to operate largely independent of a human operator. In 1957, the University of Washington’s Applied Physics Laboratory developed the Special Purpose Underwater Research Vehicle, also known as SPURV. One of the first UUVs, the SPURV was funded by the Office of Naval Research and it could dive up to 10,000 feet and operate for four hours. By the time the SPURV program ended in 1979, other ROVs and UUVs were being developed in the United States, particularly for marine exploration and research. In 1985, the Argo, a remotely operated underwater vehicle, found the wreckage of the Titanic and, in 1989, the Bismarck, a World War II battleship. As a September 15 article in the New York Times pointed out, due to the lower operating costs and ability to stay underwater for extended periods of time, UUVs remain extremely attractive option for deep sea researchers.

A 1983 article in Popular Science detailing the use of UUVs for scientific exploration. Via AUVAC.

In the 1990’s, after two U.S. Navy ships were badly damaged by Iraqi sea mines in the Persian Gulf during Operation Desert Storm, the Navy began investing heavily in Mine Countermeasures and initiated a program to build UUVs that could disable enemy sea mines. The Navy’s 1994 Unmanned Undersea Vehicle (UUV) Plan set the priorities for the program; topping the list was the need to develop UUVs for organic and clandestine mine detection. In other words, the Navy needed a system that could avoid detection by operating underwater and in advance of dedicated minesweepers on the sea surface. In 1996, Northrop Grumman developed the Near-Term Mine Reconnaissance System, a two-vehicle platform that was designed to be launched from the torpedo tube of a submarine and connected to the vessel by a fiber-optic tether. The NMRS was replaced by the Long-Term Mine Reconnaissance System. The LMRS program produced the 20 ft.-long Boeing AN/BLQ-11 drone.

The UUVs of Today

In the years since the Navy’s foray into UUVs with the NMRS and LMRS, a number of underwater drones with varying capabilities have emerged. With the release of the Unmanned Undersea Vehicle Master Plan in 2004, the Navy set out nine priorities for future UUV capabilities that ranged from disabling enemy submarines to delivering supplies and launching weapons. Among the most prolific unmanned undersea drones are the REMUS family of UUVs. The Remote Environmental Monitoring UnitS program was initiated by Woods Hole Oceanographic Institution and Hydroid, a Massachusetts-based UUV manufacturer that is owned by Kongsberg Maritime. During Operation Iraqi Freedom in 2003, a REMUS UUV was used to help clear sea mines from the waterways around the port of Umm Qasr, marking the first time a UUV was deployed in a combat environment. One of the most capable Hydroid UUVs is the REMUS 600, a torpedo-shaped underwater drone designed to carry a large payload of sensors to great depths for meteorological and oceanographic surveying. The MK18 Mod 2 Kingfish, a variant of the REMUS 600, costs the Navy around $1.3 million apiece. Although the Marine Mammal Program, the Navy’s effort to use dolphins and sea lions to detect sea mines, is still running, it is expected that the Kingfish will replace the mammals within a few years.

While the Navy has adopted an expansive view of what UUVs could possibly achieve, countering enemy mines remains a high priority. The Navy traditionally fields an array of air and sea systems to counter enemy mines, including fleets of minesweepers and low-flying helicopters that use sonar to detect mines. Instead of sending out human divers, UUVs can be used to disable the devices with a small explosive charge, eliminating the risk to sailors. UUVs are also fitted with sonar and other sensors that can autonomously map out minefields. The Battlespace Preparation Autonomous Vehicle, a Bluefin Robotics Bluefin-21, the Surface Mine Countermeasures, a General Dynamics Knifefish UUV, and the Remote Minehunting System, a Lockheed Martin Remote Multi-Mission Vehicle, all provide varying anti-mine capabilities. The Navy is under pressure to develop and deploy these systems, as existing platforms like the Avenger-class minesweeper and the Sea Dragon helicopters are quickly reaching the point for retirement. Instead of fielding specialized minesweepers, the Navy decided to couple mine countermeasures to existing manned vessels, including to the new Littoral Combat Ship (LCS), creating the MCM “module” for the LCS.

Funding for the UUVs is spread across several different procurement packages and research items. In Fiscal Year 2016, the Department of Defense is on track to spend around $232.9 million on UUVs, a $86.7 million increase over FY2015. However, this figure represents dedicated UUV spending, and does not necessarily include funds that are within larger spending packages. For example, while a large part of the Navy’s research into anti-submarine warfare surveillance, a $83.4 million project, will be spent on developing a UUV for detecting enemy submarines, the exact amount to be spent on this aspect of the program is unclear.

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On the procurement and acquisition side, the Navy’s budget for Fiscal Year 2016 includes $51.7 million in funding for the Underwater Explosive Ordnance Disposal Programs, a fund that encompasses a large number of acquisition efforts for counter-mine technology, including the MK18 Mod 2 Kingfish. Another procurement line is the Remote Minehunting System, for which the Navy has allocated $87.6 million. The main product of the RMS is the Lockheed Martin’s Remote Multi-Mission Vehicle, an autonomous UUV that is intended to be deployed from a Navy ship to help search for mines by towing a sonar. The concept for the RMS was actually developed in the late 1990s, and it has had a checkered history. According to the latest Selected Acquisition Report for the RMS, the acquisition cost of a single system has doubled from $13 million to $27.7 million between 2007 and 2015. In September, Senators John McCain and Jack Reed criticized the Navy’s Remote Minehunting System program, arguing that the RMMV had failed to meet requirements and could be scrapped altogether. Last month, the Navy formed an independent board to review the RMS program.

“Put simply, while estimated overall spending on RMS is only a little higher than originally planned, it is only yielding half the number of systems, at more than double the unit cost, and it is taking twice as long to field it,” Sen. McCain wrote in a review of the RMS program. “Also, it doesn’t work.”

Military funds for UUV research and development are likewise scattered across a number of different programs. Two of the most well-defined research programs are the Knifefish Surface Mine Countermeasure Unmanned Undersea Vehicle—$17.4 million—and the Large Displacement Unmanned Undersea Vehicle—$38.8 million. Unlike UUVs intended specifically for countering mines, the LDUUV is designed to have multiple mission types including persistent intelligence gathering capabilities and payload delivery. When Secretary Mabus announced that the Navy is planning a squadron of autonomous underwater drones last month, these were platforms that he was referring to. According to Inside Defense, the Pentagon is expected to start soliciting bids from contractors to build the new systems within the next month or so. Yet, like the RMS program, the development of the LDUUV has not always been an easy sell on Capitol Hill. The Senate draft of the FY16 National Defense Authorization Act included comments that the planned LDUUVs are “too large and expensive to deploy in quantity but are likely too small to host the systems needed for long-endurance independent operations.” The final version of the FY16 subtracted $5 million in research funding for the LDUUV.

The Department of Defense has also allocated funding for more experimental UUV programs, sometimes within larger line items. For example, a portion of the $4 million allocated to the Chief of Naval Operations Rapid Innovation Cell will go towards Project NEMO, which is developing biomimetic UUVs that can swim like a fish. The Defense Advanced Research Project Agency has allocated $32.7 million towards Hydra, a project to develop a network of unmanned underwater assets that could be deployed quickly to respond to emerging threats. Like the Hydra project, the goal of DARPA’s Upward Falling Payloads initiative, to which it has allocated $22 million, is to design affordable unmanned systems that are deployed to multiple locations and that, when remotely activated, can rise to the surface to deliver either a waterborne or airborne payload of explosives or supplies. Distributed Agile Submarine Hunting, another DARPA project to which $8.5 million is allocated, includes the development of UUV that tracks submarines.

Conclusion

Compared to unmanned vehicles in the air and on the ground, maritime drones are less well known. The impending retirement of manned vessels like minesweepers and recent geopolitical events indicate that that could change within the next few years. In the race to develop unmanned systems, other countries are pursuing UUV programs. In 2014, the Yomiuri Shimbun reported that Japan and the U.S. were collaborating on a 33-ft.-long UUV that would be used to help patrol areas using sonar along a pre-programmed route. A RAND report on Chinese drones that released earlier this year found that Beijing is funding at least 15 different university research programs into unmanned undersea and surface vehicles with particular emphasis on UUV projects. A Russian news broadcast last week unintentionally confirmed suspicions that Russia was building a long-range UUV capable of carrying a nuclear warhead.

Unmanned submersibles could provide decisionmakers with a low-cost alternative to manned platforms for missions like minesweeping that are particularly dangerous or for mission that are tedious, like patrolling. UUVs, particularly those envisioned by DARPA, could also be a way to assert military presence in an area that is difficult to reach or geopolitically contentious. Like the minesweeper concepts, UUV platforms could be built into the capabilities of existing manned platforms like the Littoral Combat Ship or submarines. Alternatively, larger UUVs may be deployed from ports and conduct missions independent from manned ships.

The development of ingenious new unmanned systems like Boeing’s hybrid UUV and UAV Rapid Deployment Air and Water Vehicle shows that the technology is progressing quickly. However, as a thorough 2009 RAND study of UUV systems pointed out, some of the Navy’s proposed UUV missions were not practicable and remained out of reach. Issues with reliability, specifically within the realms of underwater communications and propulsion, suggests that the technology has yet to mature. The controversy over the Remote Minehunting System is one example of how the Navy has struggled to develop an adequate countermine alternative to minesweepers. While UUVs present significant advantages in terms of cost and safety over manned platforms, it will be some years before UUVs are accepted into the fleet.

This post originally appeared on the Center for the Study of the Drone at Bard College.

If you liked this article, you may also be interested in:

• Autonomous underwater system for monitoring health of Venice Lagoon demos at #EXPO2015
• CoCoRo: Tracking the development of the world’s largest autonomous underwater swarm
• Ocean-going robot fleet successfully completes fish-tracking mission
• British National Oceanographic Centre launches major unmanned exploration mission
•  ROV operations expenditures to grow to $1.5 billion by 2017

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we’ll be uploading a new weekly video detailing the latest stage in its development. This week’s video demonstrates autonomous shoaling that can also be controlled.

Groups of Lily robots emit blue light, blinking signals that can be seen by other nearby robots. Those robots can then estimate their distance and angle in relation to other robots and autonomously relocate to stay together. And, if just one of the robots is radio-controlled, then the human operator can influence (and thus control) the whole swarm. However, the operator cannot move directly to the target, instead they must observe the swarm and react to it or, as Tobias who made this video, said: “You have to feel the swarm.”

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we’ll be uploading a new weekly video detailing the latest stage in its development. This week’s video shows Lily robots individually demonstrating behavior to enable coherent global swarming.

For a swarm it is crucial that the robots stay together. One method of achieving this is to generate a “virtual fence” around the swarm, as we have already demonstrated with various “confinement” videos in the Year of CoCoRo (weeks 17-19). Besides those methods, it helps if the robots can stay together – like a school of fish, a flock of birds or a swarm of mosquitos. We wanted to mimic these simple individual actions to produce  a coherent, global swarming behavior.

Our Lily robots observe blinking, blue-light signals form neighboring robots. Based on the estimated distance between the robots, they decide – with threshold-based mechanisms – which direction to turn, and how to adjust their motion speed. Although this system is very simple it nicely allows the robots to stay together for longer periods of time.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we’ll be uploading a new weekly video detailing the latest stage in its development. This week’s video shows an on-table experiment in which two Jeff robots dynamically choose between blue-light and  radio-frequency communication,

Last week we showed that our robots can also communicate in murky waters when blue light communication is blocked. We showed radio-frequency, electric potential fields and magnetism, although those channels can also be disturbed or blocked, as natural magnetic fields, electrical charges and WiFi are easily disturbed. This means that it is crucial for the swarm to dynamically choose the appropriate communication channel.

Of course, one can always just send and receive using all channels, but this might  jam them and waste energy (battery time). So a dynamic choice of just one channel is preferable and our CoCoRo robots are capable of this.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AVs) that show collective cognition. Throughout 2015 – The Year of CooRo – we’ll be uploading a new weekly video detailing the latest stage in its development. This week’s video shows a collection of algorithms and communication channels that we explored to coordinate robots in murky, turbid waters.

The individual algorithms have already been demonstrated in larger settings on Lily and/or Jeff robots in the Year of CoCoRo. However, in this compilation, we show how well the algorithms work in very murky waters by pouring milk into the pool. Yes, milk. We think these were the first milk-swarm-robotic experiments in the world! But we’re not sure – there are other crazy people out there, so you never can tell  :-)

Monitoring the ocean for pollution, ecology and climate change effects is a costly and elaborate task, especially in a complex area like Venice, with its lagoon, its many channels, cable and pipe infrastructure, industrial areas, harbor, marshland, and mussel farms. An interdisciplinary team of European scientists is breaking new ground in underwater environmental monitoring with the EU-funded subCULTron project. With a budget of 4M Euros, they are developing the world’s largest intelligent underwater monitoring system that coordinates, communicates and collects data autonomously. 

subCULTron is coordinated by the Artificial Life Lab of the University of Graz, working together with scientists from Italy, Belgium, France, Germany and Croatia. The group is developing a swarm of 120 autonomous underwater robots that will collect environmental data to help provide insight on how the Venice waters are influenced by the complex interplay of industry, tourism, transport, and local inhabitants. One of the long term goals of the project is to find ways to prevent future damage in the region.

A smart monitoring-system based on bio-inspired autonomous robots

Scientists have chosen an autonomous swarm approach for this challenging task because such systems are known to be very robust, flexible and scalable, especially in unpredictable, changing environments. Bio-inspired algorithms derived from derived from social insects, fish or slime molds are used for the swarm communication, as these systems can work without any central unit of control, and the loss of individual robots doesn’t lead to the failure of the whole system.

Another advantage of developing a self-organizing robot society is that the single robots are inexpensive and small compared to most conventional underwater robots, which makes it possible to build a larger fleet (in this case 120 robots) to maximize the sampling area and to collect large amounts of very diverse data in parallel. 

How does the subCULTron swarm work?

The subCULTron swarm will consist of three different robot types that are designed and developed especially for the project and which represent cutting edge technology and software: 

• artificialMussels (aMussels) are novel autonomous robots that sit on the ground after being deployed in the water to collect various data over a long period of time, establishing a dedicated sensory network. When necessary, they will also be able to move as a group, exploiting given energy sources, e.g., turbulences or water currents. 
• artificialFish (aFish) are fast and agile underwater robots that are used to monitor the ground and to search for given targets. They also bridge communication from aMussels to artificialPads (aPads).
• aPads are robots that float on the water surface, inspired by lily pads on a pond. They complete the communication chain from aMussels on the seabed via aFish to aPads and finally to the scientists in the lab.

The goal is to have these three different robot types running efficiently by exploiting advanced energy harvesting techniques developed by the project team.

What novel knowledge is expected from the subCULTron swarm?  

Thanks to the large number of swarm-robots, the autonomous communication system and the novel robot types, an enormous amount of environmental data about the Venetian underwater world will be collected in parallel. The hope is that this data will yield new insights into the effects of human intervention on the natural water habitat in and around Venice, helping to preserve this world heritage site from the effects of climate change.

Biologists and ecologists should also benefit from insight gained into ecological systems, and it is hoped that the tech industry will be able to build on subCULTron to innovate technology for underwater applications.

subCULTron goes to EXPO 2015

From October 15-16, subCULTron 2015 scientists will demonstrate their first robot prototypes live in small pool experiments at the Venice EXPO 2015. 

• WHEN: 15^th-16^th of Oct. 2015
• WHERE:  AQUAE VENICE 2015 Pavillion
• ADRESS: Via Galileo Ferraris, 5 , Venezia Marghera
• LINK: (http://www.aquae2015.org/where/?lang=en)

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AVs) that show collective cognition. Throughout 2015 – The Year of CooRo – we’ll be uploading a new weekly video detailing the latest stage in its development. In this week’s video we offered the swarm targets of different quality or size. 

In addition to target search, we also implemented an improved version of the “fireslime” algorithm, which is inspired by fireflies and slime mould. We have already shown that this algorithm is capable of measuring the sizes of local robot groups with just a 1-bit blinking signal. By combining the target search and swarm-size measurement  algorithms, we can generate a meta-cognition algorithm for the swarm.

First the robots search and aggregate at the targets. Then they count the size of their local aggregations and, if this number is too low, each robot knows that the swarm – as a whole – did not make a clear decision. This is clear metacognition by the swarm: the swarm is collectively aware of whether or not it knows what the best magnetic target site is.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AVs) that show collective cognition. Throughout 2015 – The Year of CooRo – we’ll be uploading a new weekly video detailing the latest stage in its development. This week’s video shows the mechanisms used in the “social inhibition algorithm” in more detail and our experiments with swarms of robots in a vertical aquarium. 

Last week we introduced the “social inhibition algorithm” we developed for our CoCoRo robot swarm. This is an algorithm for regulating division of labour, inspired by the self-organized regulation of physiological age in honeybee colonies.

The week the robots regulate an internal variable, X, by interacting with other robots, and split the swarm into three cohorts, allocating themselves to different depths in the aquarium. We added and removed robots. Their regulation of the internal variable leads to an automatic rearrangement of the swarm, so that there are always an equal number of robots at each depth.

We also added sources of interest: special robots or external lights, at different depth layers, and the swarm rearranges in a way that more robots are attracted to particularly interesting depths.

The algorithm shown here is promising due to its ability to automatically rearrange the robots to meet different demands, based only on local interactions. This makes it attractive for large scale applications where several sub-groups of the swarm need to act at different depth levels performing different tasks.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AVs) that show collective cognition. Throughout 2015 – The Year of CooRo – we’ll be uploading a new weekly video detailing the latest stage in its development. This week’s video shows the “social inhibition algorithm.” Inspired by honeybees, it’s a mechanism to regulate division of labour by socially inhibiting physiological age progression.

The more older bees there are, the slower younger bees will progress in their age-dependant work schedule. The more often the younger bees meet the older ones, the faster these older bees will progress, thus they will age faster physiologically. In this way the physiological age is self-regulated in the honeybee colony and ultimately also the division of labour.

We implemented these mechanisms into our robot swarm. Each robot holds a variable, X, representing its “physiological age.” If two robots meet, these values can change a bit, depending on their past experiences. Over time all robots will have quite different values of X. The work to be performed or, in this case, the depth to which the robot dives, is based on the value of its X.

If robots are removed or added at specific depths, the contact rates of some will change and, in turn, their X values, ultimately re-arranging the whole swarm in a homeostatic way. By casting lights at some robots we increased the “work load demand” at a particular depth layer and automatically more robots were recruited to that layer. The video shows our first experiments with this algorithm in a pool, with the robots at two different depths: near the surface and close to the ground.

The BEECLUST is a simple swarm algorithm derived from the walking and resting behavior of young honeybees, who can compare several temperature spots in their environment and collectively choose the optimal (warmest) spot. In our video, the algorithm was translated to underwater robots.

The robots move randomly in their habitat. When they meet another robot, they measure how deep the water below is. The more shallow the water, the longer they stay in place.

By running this algorithm, the swarm is able to identify shallow places and collectively choose the shallowest. The same algorithm can be used to find the deepest point, the darkest or the brightest, simply by correlating the resting time of the robots with other local environmental properties.

The BEECLUST is one of the simplest swarm algorithms possible, possibly even THE simplest. However, an algorithm like this does not suit every application: in our experiments we found that, in contrast to crawling honeybees in the hive or driving wheeled robots on the ground, it is very tricky for an AUV to stay in place in water because of drift and turbulence. Even in an aquarium this is an issue, with a number of robots moving around, so we concluded that, for a more turbulent underwater habitat, we needed a better algorithm than the classical BEECLUST.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AVs) that show collective cognition. Throughout 2015 – The Year of CooRo – we’ll be uploading a new weekly video detailing the latest stage in its development. This week we’ve once again uploaded two videos. The first is a computer animation of our “combined scenario number two” and the second shows several runs of this scenario.

In a fragmented habitat only one compartment holds the magnetic search target. In every compartment a swarm of Jeff robots searches the ground. Those that find the target inform the Lily robots who disseminate the information to other compartments, moving randomly. When the Jeff robots who did not find the target are informed that other Jeff robots in a different compartment did, they rise up to the surface, perform a random walk and then sink back down into a new compartment. Over time, the swarm converges on the place where the target was found. Upward signalling  by Jeff and Lily robots also attracts the surface station to the location above the search target.

The second video shows several runs of “combined scenario number two.” Jeff robots (on the ground), Lily robots (information carriers at all depths) and a simple surface station (we used a special Lily robot as a surrogate ) cooperate to identify the compartment containing the magnetic search target. The robots perform the same algorithms, or behaviors, in the computer animation. Initially, each one of the four compartments holds one Jeff robot. At the end of the run, three of these robots are located in the compartment with the target, together with a number of Lily robots and the surrogate of the surface station. The remaining Jeff robot could not reach the target compartment for mechanical reasons, although it tried several times, as indicated by the green LED signals it shows from time to time. This is a good example of how things work in swarm robotics; there are always robots that don’t perform well but they do stay functional as a collective.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we’ll be uploading a new weekly video detailing the latest stage in its development. Over the last four posts we demonstrated how the robots use a relay chain to communicate between the sea ground and the surface station. The following two videos show an alternative to this communication principle. The “relay swarm” scenario uses a swarm of Lily robots performing random walks in 3D for transmitting information about the status of the search swarm of Jeff robots on the ground. 

This first video explains the scenario in a computer animation:

The second video shows the real-world experiments performed in the “relay swarm” scenario. First Jeff robots search the ground of a fragmented habitat for a magnetic target. As soon as it finds the target it signals this locally with blue-light LEDs. Lily robots that also roam the habitat can pick up the signal from this Jeff robot. The info can also spread from Lily robot to Lily robot as they meet, spreading like an infectious process. Finally, Lily robots inform the surface station that the Jeff robot on the ground has found an interesting target. Future extensions foresee that after informing the surface station another phase starts: a second signal spreads from the surface station through the Lily robots back to the Jeff robot on the ground, and ultimately makes the Jeff robot to go up to the surface above the found target.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built the world’s largest swarm of 41 autonomous underwater vehicles (AVs) that show collective cognition. Throughout the first half of 2015 – The Year of CoCoRo – a new video on the latest stage in its development has been uploaded every week, with dozens online to date and more to come! This article provides some insights and background about some of the videos already online.

Carried out by an interdisciplinary and international team of scientists from Austria, Italy, the UK, Belgium and Germany, the CoCoRo project ran from 2011-2014 and has its own YouTube playlist. It offers a novel information platform for the scientific community and anyone interested in how new high-end technologies are developed and what impact they could have on people’s lives.

The footage tracks the development of this unique, autonomous swarm and also demonstrates how it gained the necessary self-awareness to solve tasks such as search missions on the seabed.

In the videos already online, three different autonomous underwater robots, designed and produced for the project, are introduced: very agile and fast Jeff robots, which search the waterbed, and smaller Lily robots, functioning as communicators to relay the information found by Jeffs to the third robot type: a base-station which floats on the water surface and is able to pass information to scientists on the ground.

The videos also show many different experiments, showcasing the broad functionality of the autonomous swarm. They demonstrate how novel, bio-inspired algorithms, developed by the team, give the swarm a form of self awareness to provide a much broader knowledge base than is in the possession of each single member. This enables the swarm to make collective decisions.

Thus, the videos demonstrate the functionality of the swarm, in collective search missions, as well as the abilities and the specialties and strengths of the different robot types. Below, we look at some of the highlights of this research.

Autonomously driven camera agents, exploring seas, rivers and ponds

The Jeff and Lily robots were tested as autonomous diving robots with cameras mounted to collect material on the underwater ecosystem. Their recorded diving trips provide lively insights into the underwater world: the sea, mountain lakes, ponds and rivers. Although not the main focus of the project, this work developed into a set of interesting spin-off experiments. It is always nice to discover that a tool can also be used something other than what it was originally designed for. In the first set of experiments, an underwater camera was mounted on a Lily robot. The setup was initially tested in a small fishpond, then in a larger Austrian lake and, finally, some mad scientists even threw the poor robot into a whitewater river!

Once it was discovered that the robots could be used as autonomous camera agents, cameras were also mounted on the Jeffs and the research team moved on to larger lakes and more complex habitats to get an inside view.

Blue light and electric sensing for novel AUV underwater communication

The CoCoRo team established a new approach to underwater communication that is showcased in the videos.

In contrast to the majority of underwater swarms, these robots don’t communicate using sonic or echo technology but instead make use of blue-light LEDs, WLAN and potential electric fields. Blue-light blinks are used to keep the swarm together and in the vicinity of the moving base station. Because of this “confinement,” the radio-controlled base-station can pull a whole swarm of Lily robots behind it, much like a tail. In larger water bodies, it is important to confine the robots into specific areas because, if agents are to work efficiently, the swarm normally requires a minimum of connectivity that is achieved only with a critical density; if they’re not confined to a controlled area, robots could get lost and the density could fall below the critical level. Thus, confinement was identified as a key function.

“Electric sense” (coupled potential electric fields) is another important CoCoRo feature. An electrode was placed underwater, beneath the CoCoRo surface station, to generate a pulsing electric field. Jeff robots, with electrodes attached to their outer hulls, are able to sense such fields. In this way, it’s possible to confine the robots to a specific area, around the base station, so that they don’t go astray. Electric-field confinement was tested in a pool, under laboratory conditions, and then in the Italian harbor of Livorno.

Self-charging mechanisms guarantee long-term autonomy in energy

An important goal of the CoCoRo project was to develop an underwater swarm able to achieve long-term autonomy in respect of energy. To this end, a special docking/undocking mechanism was constructed for Jeff robots, on the base station. Later versions of these robots that run out of power will not only be able to dock or undock autonomously there, but also recharge. Several videos demonstrate this docking mechanism in action, both in a pool and in the Mediterranean.

Collective decision making on underwater search missions in highly fragmented areas

Another important aspect of the CoCoRo swarm is that it is able to make collective decisions, the result of a set of bio-inspired algorithms developed for it. To test this ability, a set of collective choice experiments was performed.

The Lily swarm, as a whole, was tasked with choosing a global magnetic optimum over several local optima. To make the experiment even trickier, and the results more clearly interpretable, the scientists constructed a T-Maze offering only a binary choice. In another experiment a similar task was performed in an open field. In each of these settings, the swarm reached a clear-cut decision in favor of the global optimum. In CoCoRo, the ability to make a decision is a property of the collective, not of the individual.

However, habitats are often more structured than a T-maze. To test the capabilities of Jeff robots in highly structured environments, area coverage was achieved through autonomous motion behavior; a specific random walk, with the robots browsing through the habitat, performing a simple program named “correlated random walk with obstacle avoidance.” This consists of a straight-forward motion alternated with infrequent, randomly triggered turns and collision avoidance based on blue-light emission and the robot’s sensing of its reflection. As a result, the first robot is able to traverse the habitat relatively quickly, and subsequent robots even faster. The experiments demonstrate that, here, scalability is key in generating a viable robot swarm. To gain further insight into how Jeff robots navigate through highly fragmented areas, the researchers equipped them with cameras to film their behavior.

In order to further complicate the Jeff robots’ task of searching and navigating, the team constructed a fragmented habitat in a pool, with four compartments separated by medium-height walls. Only one of these contained a magnetic/metallic object, acting as a search target. To complete the experiment, the random-walk based motion program was occasionally interrupted by “jump-over-the-wall” behavior. This allowed the test robot to switch between compartments when it detected a near-collision event via its blue-light LEDs.

The autonomous robot observes its internal magnetic sensor in order to identify whether it is close to the magnetic/metallic target. When it is, it stops, descends and switches on all of its LEDs to mark the spot.

In another experimental video, the scientists demonstrate the use of the search algorithm in pools with increasing volumes of water and with a number of Jeff robots. Building on the previous experiment, social swarm behavior is exhibited as the robots attract each other to the target after they have found it. In a preliminary experiment, for the first time the robots went deeper than two meters, although software bugs were an issue. However, these late-night experiments brought to light crucial insights and allowed the team to develop the more complex search scenarios that will feature in the second half of the Year of CoCoRo.

In another extension of the scenario, a swarm of Lily robots was introduced, with one Lily emulating the behavior of the base station on the water’s surface. A searching Jeff robot on the ground first autonomously looks for a magnetic target. When it finds this, it stops and switches on its vertical blue-light and red LEDs. This attracts some Lily robots browsing near the surface. They pick up the signal from the ground and recruit other Lily robots with their horizontal blue-light LEDs. This attracts even more Lily robots, forming a positive feedback loop through the growing aggregation of robots. Ultimately, all the Lily robots meet at the target spot as well as the one emulating the surface station.

What’s next in The Year of CoCoRo?

In the second half of The Year of CoCoRo, the videos will emphasize more complex waterbed search tasks, showing several different approaches to this. In addition, they will focus on swarm-level self-awareness, internal homeostatic self-regulation and self-repair. The CoCoRo team will also demonstrate shoaling behavior, how the swarm can communicate, even in murky waters, and that it’s not tricked easily, not even by its own mirror image; it knows whether what it “sees” is a reflection of itself or another robot swarm. Aside from scientific experiments, further videos will cover special CoCoRo events, providing some humorous insights into EU-project driven research. Some of these will include: an exhibition at CEBIT (a consumer electronics fair) overnight project workshops and overview videos to round off the big picture: The Year of CoCoRo.

CoCoRo evolves into subCULTron

The experiments and developments shown in The Year of CoCoRo are being fed into subCULTron, a follow-up project that has already started. Here, an even larger swarm of robots – novel types with more bio-inspired algorithms – are being developed. Leading scientists from other projects, including CADDY and ANGELS have joined the consortium. This CoCoRo offspring is planning missions in the Venice lagoon, as well as in a complex, structured, aquatic mussel farm. In these environments, the water is deeper and murky, and the habitat even more fragmented, posing ever-greater challenges for this novel collective system.

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The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we’ll be uploading a new weekly video detailing the latest stage in its development. This video shows a set of experiments that investigate the capability of the relay chain (formed by Lily robots) to transmit (relay) information between two spatially separated places.

At one of these places we trigger a special RF (radio frequency) pulse to be emitted by a robot. Neighboring robots that receive this pulse send out a similar pulse, relaying the signal along the chain. To control the directionality of the spreading signal, there is also a refractory period after each relaying act in which the robot is unreceptive for the relayed signal. This system is inspired by slime mold amoebas and giant honeybees and serves very well for the underwater communication purpose.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we’ll be uploading a new weekly video detailing the latest stage in its development. This video shows a series of experiments we performed on the relay chain, a chain of Lily robots that connects two spatially separated points in the underwater habitat.

In the first set of experiments these two end points are fixed, while in the second set they are moving, thus the relay chain formed by the robot swarm has to reconfigure and reshape. The robots in the chain perform an algorithm inspired by the shoaling behavior of fish. Using their photoreceptors (photodiodes), they look out for the blue-light blinks of neighboring robots and estimate distance and angle from the light intensity. This helps them decide which direction to follow.  The algorithm can also function “communication free,” using, instead, an onboard camera, with image processing replacing the sensing activity and no need for signals from neighboring AUVs. Operating with passive sensing is important for the scalability of the method.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we’ll be uploading a new weekly video detailing the latest stage in its development. This week, we show several instances of a relay chain, in the middle phase of our “combined scenario #1” 

In the CoCoRo system there is often a large gap between the swarm of Jeff robots on the ground, that have found a search target, and the base station at the water surface. Underwater communication is often difficult and limited so, to bridge this gap, a swarm of Lily robots build a chain, which we call a “relay chain.” It allows the ground swarm and the surface station to communicate with each other through light pulses, RF or electric pulses. In this scenario we test information transfer in both directions by triggering either the ground swarm or the base station with a flashlight pulse.

The relay chain is formed by Lily robots executing a special “shoaling algorithm.” Inspired by fish shoaling behavior,  It is based on their reactions to their nearest neighbors sensed passively by photodiodes on the outer hull of the Lily robots. Reception of periodic blue-light blinks allows the robots to detect each other and to align and orient themselves relative to their neighbors’ configuration. Thus, the relay chain formation itself is almost communication-free, as the photodiodes and blinks can be easily replaced by a video camera and object detection. The only actual communication is the relaying of RF pulse information, based on the strategy used by slime-mould amoebas.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we’ll be uploading a new weekly video detailing the latest stage in its development. This week, in another important phase of our “combined scenario #1, the Jeff robots perform a collective search on the water bed for a sunken metallic/magnetic target object.

We conducted an extra series of experiments on this phase of “combined scenario #1” to test the efficiency and robustness of the autonomous swarming behavior. When the object is located, other robots are recruited to the spot by horizontal blue-light signals, Ultimately, there’s an aggregation of Jeff robots around the target that marks the location for the Lily robots who then form a “relay chain” to the surface station.

Following this event, the only human intervention in the execution of the scenario ocurrs: a button pressed by a human operator sends a WLAN signal and the swarm is released. For this very important security measure, we purposely kept the human in the loop, because a robot swarm is just a tool that a human operator, who is responsible for it, can use to achieve or facilitate a certain task. After the release of the swarm, the Jeff robots sink to the base of the pool and begin their search for the metallic/magnetic target object. Meanwhile, the Lily robots swarm out to build the “relay chain” between the target spot and the surface station.

In CoCoRo, the search for a sunken metallic object on the waterbed was dealt with in several scenarios. The first video is a computer animation illustrating the phases of the most complex, “scenario #1:”

1. The base station at the water surface moves into the habitat, with docked Jeff robots and a swarm of Lily robots confined to it.
2. The Jeff robots are released. They sink to the ground and search the habitat.
3. As soon as the first Jeff robot has found the target, it recruits other Jeff robots to the location via blue-light LED signals, leading to a positive feedback loop that attracts more and more Jeff robots to the area.
4. Meanwhile, the Lily robots build a chain that connects the surface station to the aggregated Jeff robots on the ground.
5. Through this “relay chain,” information (blink signals or other data) can be sent from the ground swarm to the surface station (and human operators) and vice versa.

Scenario #1 combines many of the individual algorithms and functionalities shown in previous videos in “The Year of CoCoRo.”

The second video shows how this scenario was performed by real robots in the water. It is noteworthy that all the phases in the scenario were executed in one single run and not split into different experiments. It was important for us to demonstrate that, using the modular algorithmic design of CoCoRo, it is possible to build up complex behavioral programs to solve composite tasks.

To date, in bio-inspired and swarm robotics, there have either been simple algorithms to enable robots to perform basic tasks or complex scenarios achieved by very complicated and specifically written software. To our knowledge, this is the first time in swarm robotics that several very simple signal exchange patterns and behavioral responses, triggered by simple signals within the environment, are combined with an elementary piece of code that allows the robot swarm to perform a very sophisticated behavioral program. There is no recourse to global knowledge, mapping, ego-positioning, image tracking or computer vision within this autonomous swarm. There are only: a few blue-light blinks, photoreceptors to receive these, random-walk programs, communication-free shoaling-behavior for the relay chain, radio-frequency pulsing and a simple internal-robot compass. This is how swarm robotics should be: simple, robust, flexible, scalable and – most importantly – it works!

Our test of “dead reckoning” was performed by a Jeff robot in Livorno harbor. The robot was programmed to follow a straight-line trajectory, then perform a circular search pattern (including collision avoidance) and end with a direct return to the starting point. The robot used only a magnetic compass sensor, an internal clock and blue-light LEDs for collision avoidance. Considering the simplicity of the technology, we were surprised at how efficiently it performed, despite the currents in the harbor and occasional collision-avoidance behavior triggered by the buoys.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we’ll be uploading a new weekly video detailing the latest stage in its development. This video shows the robots finding magnetic targets.

A heterogeneous swarm of Lily robots, Jeff robots and a surrogate for a surface station were used for this experiment. The search target was again a magnetic target in a fragmented habitat. The Jeff robot first autonomously searches for the target, stops when it’s located and switches on its vertical blue-light and red LEDs. This attracts Lily robots browsing near the surface. They stop and recruit other Lily robots with their horizontal blue-light LEDs. This attracts even more Lily robots and establishes a positive feedback loop – a growing aggregation of robots. Ultimately, all the Lily robots aggregate at the target spot, together with the one that emulates the surface station.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we’ll be uploading a new weekly video detailing the latest stage in its development. This video shows four autonomous Jeff robots performing a random-walk based search program in a large pool.

After we tested our search algorithm for magnetic targets in the small pool, we set out for one with several times the water volume. This time, we used four Jeff robots, performing a random-walk based search program. Although we experienced many problems during the  long night of the experiment, we did achieve some success with this extended search scenario. Our issues were mostly software related: the robots were running along cyclic trajectories instead of doing an efficient random walk. These were problems which we experienced in early summer 2014 and later in the year we made significant progress, as later videos will show. Although the finding rate was not perfect, the robots still managed to locate the target on several occasions, and it was the first time we operated our robots successfully at depths below 2m.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. This video shows the agile, autonomous robot, Jeff, in a search and exploration task for a magnetic target. 

We constructed a fragmented habitat in a pool, providing four compartments separated by medium-high walls. One compartment contained a magnetic/metallic object, acting as a target. A Jeff robot performed a search pattern using its internal magnetic sensor to identify the target. Once it achieves this, it stops, sinks on top of the target and switches on its LEDs to mark the spot.

The video shows our experiments to see how many positive results were achievable in a row. The robot’s reliability, given that there is no optimized search function or higher-cognitive capability (no map, camera, memory or plan), is quite a surprise!

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. This video shows autonomous a Jeff robot working as an underwater autonomous camera agent.

We put an underwater camera on one of our Jeff robots while it was diving autonomously in a structured, in fact highly fragmented habitat. This let us see what it looks like to be inside of the swarm and also if we can see into the hidden places by putting a camera on an autonomous, purely “random walking” robot. As we have already shown in previous weeks, we extended this approach also to natural habitats, including lakes and an ocean harbour. As shown in the video below, we tested the areal coverage of the swarm and saw how many interesting sites will be caught on camera.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. This video shows autonomous Jeff robots as they explore a complex debris field.

The important issue here is the area coverage that can be achieved by the autonomous motion behavior. We found that a simple program called “correlated random walk with obstacle avoidance” does the job quite well. This program is straight forward motion altered by infrequent randomly triggered random turns and collision avoidance based on blue-light emissions and reflection at the nose of the robot. One robot covers the habitat quickly, and more robots do it even faster. This scalability is important to generate a useful robot swarm.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. This video shows a swarm of underwater robots working together to search and make decisions collectively .

Based on the algorithm we presented in the previous video on aggregation magnets, we conducted a series of collective choice experiments with our Lily swarm robots in two distinct environments: 1) open field, which asked for choosing a global magnetic optimum over several local ones, and 2) a T-Maze setup, which offered only a binary choice. In all experimental settings the swarm reached a clear decision in favor of the global optimum. This ability of decision making is a property of the collective, not of the individual.

This is made difficult by water currents that cause single robots to drift away after they have located the target. By recruiting other robots to the spot using blue blinking lights, a larger group can form at the site. This stabilizes the swarm and prevents any unneeded robots from drifting away. Collectively, the robots manage to solve the task using a basic behavioral program. This shows that even very simple agents are able to perform complex tasks collectively, with no recourse to global knowledge.

To learn more about the project, see this introductory post, or check out all the videos from the Year of CoCoRo on Robohub.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. This video shows the Jeff robots docking and undocking autonomously in Livorno harbor.

After a successful test of autonomous docking in our pool, we then tested this functionality  in Livorno harbor. Despite all the waves, turbulence, wind, rain and other challenges, docking was very reliable and fast. We could also use docking to deploy a robot to a specific site with our radio-controlled surface station. Bear in mind: docking and undocking is autonomous, as is the Jeff robot – as soon as it undocks from the base station.

To learn more about the project, see this introductory post, or check out all the videos from the Year of CoCoRo on Robohub.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. This video shows the Jeff robots docking and undocking autonomously with the surface station.

For the sake of achieving long-term energy autonomy with our CoCoRo system, we constructed a docking/undocking mechanism for Jeff robots on our surface station. This functionality was first tested with a fixed mounted docking device, and then with a docking device floating around in our pool. The autonomous docking worked exceptionally well under all conditions.

To learn more about the project, see this introductory post, or check out all the videos from the Year of CoCoRo on Robohub.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. This video shows how we used an electric underwater field in Livorno harbour to field test confining the robots to a specific area around the base station so that they don’t get lost.

After having tested the electric-field confinement of the Jeff robots to the base station in our pool, we went out to Livorno harbour to test it under out-of-the-lab conditions. Although our CoCoRo prototype robots were not designed to operate in salty ocean water — there is a significantly different electrical conductivity compared to freshwater — the electrical confinement worked there quite well.

To learn more about the project, see this introductory post, or check out all the videos from the Year of CoCoRo on Robohub.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. This video shows how we used an electric underwater field to confine the robots of a specific area around the base station so that they don’t get lost.

We use a submerged electrode below the CoCoRo surface station to generate a pulsing electric field underwater around this station. The Jeff robots have electrodes on their outer hull to be able to sense such fields. This way we can confine the robots into a specific area (volume) around the base station. This is important to keep the swarm together in the water, otherwise robots can get lost. We first tested this system in a pool, as it is shown in this video here.

To learn more about the project, see this introductory post, or check out all the videos from the Year of CoCoRo on Robohub.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. In this video we use blue-light blinks to keep the swarm together and to keep it in vicinity of the moving base station.

Due to this “confinement”, the radio-controlled base-station can pull a whole swarm of Lily robots like a tail behind itself. It is important to confine the robots into specific areas in larger water bodies because the swarm requires normally a minimum connectivity among agents to work efficiently, which is achieved only with a critical minimum swarm density. Without keeping the robots in a controlled area, robots could get lost and the robot density could fall below the critical density. Thus, confinement was identified to be a critical functionality.

To learn more about the project, see this introductory post, or check out all the videos from the Year of CoCoRo on Robohub.

The EU-funded Collective Cognitive Robotics (CoCoRo) project comprises the largest autonomous underwater swarm in the world. Following three-and-a-half years of intensive research, CoCoRo’s interdisciplinary team of scientists from universities in Austria, Italy, Germany, Belgium, and the UK built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Swarm members are not only aware of their own surroundings, but also of their own and other vehicles’ experiences. Throughout 2015 – The Year of CoCoRo – the research team will be uploading a new weekly video detailing a new stage in its development and reporting updates here on Robohub. Check out the first ten videos below!

Overview of the CoCoRo system

The swarm consists of Jeff robots (the highlight of the project),  20 smaller (and slower) Lily robots, as well as a base station at the surface. The setting is shown in a short overview video.

In this simple form of collective self-awareness, the swarm processes information collectively, such that, as a whole it knows more than any single swarm member. The swarm not only collects information about the environment, but also about its own state. As well as staying together as a swarm, it’s also capable of navigating and diving, searching for sunken targets and communicating and processing information as a group.

Research highlights

The swarm communicates findings via a self-established bridge to the “world above”

Not only can the swarm interact with itself, it can also communicate its findings and inner state to the world above. It does this by establishing, self-maintaining and even repairing a bridge between the swarm, located on the sea bed, and a human-controlled floating station that is located on the water’s surface.

During the project, many algorithms were developed and tested with the CoCoRo prototype swarm. Future applications include environmental monitoring and oceanic search missions.

Three-layer decentralized swarm design

When designing the swarm, the scientists decided to follow the KISS principle — Keep it Simple and Stupid. A decentralized approach was chosen for the CoCoRo project because in a swarm there is no single point of failure. Swarms are robust, flexible and scalable systems that can adapt easily to changing environments. Additionally, the technical and cost requirements for a single robot are less than in non-swarm systems.

Three different layers were implemented for the cognition: individual, group and global. Single AUVs collect information, local groups share and compare it and, finally, the whole swarm makes collective decisions.

Bio-inspired algorithms build core functions

While designing the software, the scientists decided to focus on bio-inspired algorithms because they are known to be very flexible and robust. The focal animals were slime mold, fireflies, honeybees, cockroaches or fish. The algorithms were programmed in a modular way; self-organizing cognition algorithms were merged with self-organizing motion algorithms that were inspired by various animals. In this way, totally new algorithms emerged.

Three types of AUVs communicate via a novel multichannel underwater network

A swarm of 41 prototype AUVs was built for the CoCoRo project, consisting of three different types of AUVs that range in size from a human hand to a man’s foot. Depending on their activity, the robots run from two to six hours without required a charge. A unique feature of the CoCoRo project is that sensors were implemented in a combination never used before in underwater robotics. These combinations were necessary to allow the construction of a autonomous underwater swarm that can coordinate mainly through simple signalling between nearest neighbors. In this way, a totally new heterogeneous underwater swarm system was created, consisting of three different types of AUVs: “Jeff” robots that search the sea bed; a base station on the water’s surface (connected to humans); and “Lilly” robots that bridge and communicate information between the Jeff robots and the base station.

Check out our other videos below!

Introducing Jeff

The Jeff robots are extremely agile and can resist water currents of 1m/sec. They have autonomous buoyancy control, lateral and vertical motion with optimized propellers and a rich set of sensors. For all these functions, novel energy-saving methods were implemented that guarantee autonomy and durability.

Jeff in turbulent waters

Jeff’s body shape and actuation also allows it to operate in turbulent waters, as is tested in the following video with a remote-controlled Jeff. In this video it was purposely driven into the most turbulent areas to see how it would be affected by the water currents.

Feeding Jeff with magnets

Even in turbulent water, Jeff can be controlled with enough precision to reach a specific point in 3D space. This is demonstrated by holding a small magnet in the water for Jeff to pick up.

The Jeff swarm explores its habitat

The researchers have produced 20 Jeff robots, able to swarm out and search complex habitats in parallel, as shown in the following video.

Collective search by Jeff and Lily Robots

An important aspect of the project is that all three types of robots help each other in the performance of their collective task. To achieve this, the scientists took inspiration from nature and combined several algorithms to generate a new collective program for the whole swarm. An example of this is their collective search for a magnetic (metallic) target on the sea ground, shown in a pool-based scenario. In this setting, a target is marked by a metal plate and some magnets. Several blocking objects, surrogates for debris and rocks, produce a structured habitat. The Jeff robots, on the ground, first have to spot the target by doing a search and observing their magnetic sense. After the first robot finds the target, it attracts others through the use of blue-light signals. The aggregation of Jeff robots then summons a swarm of Lily robots at higher water levels, which serve to pinpoint the site to humans.

Swarm-size awareness exhibited by Lily robots

It’s important for a swarm of robots to know its own size. In CoCoRo, this was achieved by a novel algorithm inspired by slime mold amoebas; in this case, however, the chemical signals amoebas exchange were substituted in the algorithm for blue-light blinks. These are transmitted by the robots to their neighbors and propagated to other robots in a wave-like manner. This behavior was first tested on the smaller Lily robots.

Swarm-size awareness exhibited by Jeff robots

After the algorithm was successfully tested on Lily robots, it was further polished to improve its reaction speed and prediction quality before it was implemented in Jeff robots, as shown by the following video.

Flocking by slime mold

The slime mold inspired algorithm doesn’t just allow the robots to know the size of the swarm. Combined with directed motion, it can also be used to generate slime mold-like flocks of underwater robots.

Emergent taxis of robots

After adding an additional environmental light sensor, this bio-mimicking collective behavior is turned into something the scientists call “emergent taxis.” When the entire swarm of robots run uphill in a light gradient, although individual members can’t read it (having only a very rough sensor impression of the local area), the swarm turns into a kind of moving compound eye, with all the robots observing their local environmental and influencing each other. Finally, an algorithm designed for counting swarm members transforms it into an acting organ, what might even be called a “super-organism”.

Outlook

The CoCoRo project developed and tested a whole set of “enabling technologies”, mostly in pools but sometimes in out-of-lab conditions. When the project ended in late 2014, it was clear to the research team that these technologies should be further developed and brought out of the lab. As a next step in their long-term roadmap, the research team (with additional partners) started a new project called “subCULTron” to develop the next generation of swarm robots and apply them in areas of high impact: fish and mussel farms and the Venice lagoon.

New footage of CoCoRo available on YouTube each week

During the runtime of CoCoRo, the swarm developed an enormous variety of functionalities, which were presented in several demonstrators at the project’s final review. A lot of footage was recorded and many videos were produced. These films not only give a deep insight into the different features of the CoCoRo swarm as it evolved, but also provide a behind-the-scenes glimpse into the scientific work and development that went into the project. The CoCoRo team will be uploading a new video on YouTube each week until the end of the year to celebrate the “The Year of CoCoRo” – stay tuned on Robohub for regular updates.

Links

CoCoRo Homepage
CoCoRo on Youtube
CoCoRo on Facebook

• 3’600-strong underwater robot sensor network returns 1’000’000th measurement, has scientists craving for more
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• Surgical micro-robot swarms: Science fiction, or realistic prospect?
• Underwater Dreams: The story of undocumented teens who win underwater robot competition
• Crowdsourcing new strategies for cancer treatment: Towards swarming nanobots
• Robots Podcast: Termite-Inspired Construction

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

]]> https://robohub.org/the-year-of-cocoro-video-1652-jeffcam-in-a-lake/ Sun, 19 Apr 2015 22:25:32 +0000 http://robohub.org/the-year-of-cocoro-video-1652-jeffcam-in-a-lake/

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. In this video a Jeff-Cam takes us on a trip below an Italian lake.

Jeff is much more agile and powerful then Lily. So mounting a camera ontop of an autonomous Jeff robot produced an even better autonomous camera agent. After some preliminary tests and tuning in a pool, we went to an Italian lake to see how it looks down there below the surface.

To learn more about the project, see this introductory post, or check out all the videos from the Year of CoCoRo on Robohub.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. In this video, we take Lily-Cam into rough water.

Austria is home not only to beautiful lakes, it also has wild rivers and creeks. After “Lily-Cam” did its job in the lakes, we threw it also into a fast whitewater river. We were lucky to catch it downriver after some minutes and the robot survived this adventure. However, our engineers, who have also to constantly maintain and repair the robots, didn’t like the movie. ;-)

To learn more about the project, see this introductory post, or check out all the videos from the Year of CoCoRo on Robohub.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. In this video, the Lily robot takes us below a lake in Austria.

After our successful application of “Lily-Cam” in our small ponds, we went further. At several lovely places at Styria (Austria) we took a look below the water surface and encountered beautiful and pittoresque landscapes, fish and even diving ducks.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. In this video, the Lily robot gives us an underwater view of a fishing pond.

Lily is an autonomously diving robot. After we attached an underwater camera onto it, it became an autonomously driving camera agent. We used this “Lily-Cam” to look into the little fishing ponds that we have at our Zoological Department. The look below the surface offered a fascinating glimpse of the underwater world, including algae forests, fish and other water organisms.

To learn more about the project, see this introductory post, or check out all the videos

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. One day in early 2014, at one of those long workshops, we stayed at a hotel that had a large and deep outside pool. It was a nice day in Italy, so what else could we do than taking a JEFF robot in autonomous driving mode to this pool and have some fun with it?

It didn’t take long and more and more hotel guests gathered and watched. Special applause to Vega and Finn, the kids of the project coordinator, for helping with the filming (above and under the water), catching the robot and rescuing it sometimes from the ground after we pushed the robot beyond its limits.

There will be more detailed videos following throughout this year showing some of those activities more specifically. The activities shown here show autonomous robots in larger outdoor pools, ponds, lakes, rivers and ocean harbours. They act there either alone (e.g. as autonomous underwater camera agents) or in smaller groups (swarms).

To learn more about the project, see this introductory post, or check out all the videos from the Year of CoCoRo on Robohub.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. In this video we test Jeff’s ability to pick up a target (the magnet) while swimming in a current.

In this video we again generate water currents and turbulences by using a water hose in our outdoor pool. By radio-frequency control we navigate a Jeff robot remotely in those currents. To test the precision of the steering under these conditions we use some small magnets in our hands as targets that have to be picked up by the Jeff robot. Thus it looks like we feed the robot with magnets :-)

To learn more about the project, see this introductory post, or check out all the videos from the Year of CoCoRo on Robohub.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. In this video we generate some water currents and turbulences by using a water hose in our outdoor pool. 

The video shows our first tests of the Jeff robot performed under such conditions (performed in summer 2013 and spring 2014). Those tests clearly indicated that the maneuverability of the robot (steering, forward drive) is strong enough to compensate for currents and drifts up to approximately 1m/sec. In those days we were very happy to realize such impressive capabilities for such a small robot, as this maneuverability is a prerequisite for the good performance in an underwater swarm under out-of-the lab conditions (large outside tanks, lazy river arms, ponds, lakes).

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. This video shows how a swarm of Lily robots can form a coherent group by exchanging light pulses among the group members, similar to how slime-mold does in biology.

By modulating the frequency of these signals the group can alter the path of the emerging blinking wave to turn the whole group towards the aggregation target. Such a target can be any form of gradient emitting source, regardless of the type of the emitted signal. We demonstrate this here by using a light source as a target.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. This video shows how signal waves can be used to keep a swarm of Lily robots together as a group.

A group of Lily robots can achieve a coherent shoaling or flocking configuration by emitting and receiving pulsed light signals. Similar to slime mold or fireflies, such pulsed signals are relayed from one agent to the next, forming signal waves that move through the whole swarm. We use such waves to keep the swarm of Lily robots together as a group, to coordinate the swarm and to move it in a desired direction.

To learn more about the project, see this introductory post, or check out all the videos from the Year of CoCoRo on Robohub.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. This video shows the Jeff robot using an algorithm to estimate the size of the swarm.

It is important for our robot swarm that the swarm as a whole is aware of its size. We use a bio-inspired method, called the „fireslime algorithm“ to achieve this form of collective awareness. The algorithm makes the robots to spread a one-bit signal (pulse) among the swarm members allowing them to make quite reliable and precise estimates of the size of their swarm. This video shows an advanced version of the algorithm implemented on Jeff robots.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. This video shows how the swarm can estimate its own size.

Lily robots build swarms that change in size over time. By using a bio-inspired method of signal exchange these swarms can make reliable estimates of their own swarm size. Our Lily robots emit a pulsed signal that is relayed by other Lily robots in the swarm, just like slime mold amoebas or fireflies relay their signals in nature. Based on this simple signal exchange every member can estimate the number of other swarm members around it.

To learn more about the project, see this introductory post, or check out all the videos from the Year of CoCoRo on Robohub.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. This video shows how, in a complex underwater habitat, a swarm of Jeff robots searches for a magnetic target and then the communicate to Lily robots at smaller depths to join the group.

To learn more about the project, see this introductory post, or check out all the videos from the Year of CoCoRo on Robohub.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. In this video, a swarm of Jeff robots is browsing the environment.

They use their front blue-light sensors to detect and avoid obstacles.

To learn more about the project, see this introductory post, or check out all the videos from the Year of CoCoRo on Robohub.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development.The CoCoRo system is currently the largest autonomous underwater swarm in the world. This video briefly shows some of its components and functions.

To learn more about the project, see this introductory post, or check out all the videos from the Year of CoCoRo on Robohub.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. Our CoCoRo system consists of 3 types of robots. One is Jeff, a very fast and agile small autonomous swarm robot. This is an overview of its capabilities.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. This video shows the CoCoRo autonomous underwater vehicles (AUVs) in a large aquarium at the CeBIT exhibition, and simulates a real mission scenario in which the AUVs have to collectively find an object of potential interest at the sea floor (e.g., a black box of a crashed plane).

In the beginning the AUVs start randomly distributed in the mission area (= aquarium) where the object is assumed to be. The AUVs do not perform a special search pattern but instead use a simple random walk. Due to the high number of AUVs, quite soon one of the AUVs will be near the object (in this case a magnetic target) and can perceive it.

The AUV then tries to keep its position above the object and starts to emit blue-light signals that attract other nearby AUVs which in turn also emit blue-light signals. This leads to a fast aggregation of most of the swarm AUVs near the object. In future underwater swarms these AUVs could then collectively grab and lift the object to the water surface.

To learn more about the project, see this introductory post, or check out all the videos from the Year of CoCoRo on Robohub.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. This video was filmed with an underwater camera (Sony AS100V) in a pool.

To learn more about the project, see this introductory post, or check out all the videos from the Year of CoCoRo on Robohub.

The search and rescue team involved in the recent South Korean ferry tragedy has deployed a remotely-operated underwater robot to help in the investigation and hopeful search for possible survivors in what has been called the country’s worst maritime disaster in forty years. The Sewol ferry sunk off the coast of South Korea on April 14, 2014 carrying 476 passengers and crew on board; of these, 339 were high school students and teachers on a field trip. While 174 people have been rescued, divers’ efforts to locate the remaining victims have been hampered by strong currents, cold water and floating debris. An underwater ROV by Deep Trekker has been deployed to help rescuers assess the wreckage and the surrounding sea floor before sending divers into the ferry’s interior.

Hwang Dae-Sik, a diver at the scene, explained to Reuters the immense difficulty of his task: “We are trained for hostile environments, but it’s hard to be brave when we meet bodies in dark water … This is the most gruelling and heartbreaking job of my career.” According to the Reuters report, divers have been navigating the dark corridors of the ferry while carrying tethered oxygen tanks and communication lines that can snag on the sharp corners of the wreckage’s interior. Divers can only stay underwater for about an hour at a time.

“My heart goes out to the friends and families of these victims,” said President of Deep Trekker Sam Macdonald. “We hope our ROV can help keep divers from being put at further risk and that the families will get some closure.”

Websolus Co., Deep Trekker’s South Korean distributor, had a demo of the DTG2 ROV on hand and was able to quickly deploy the device and their expertise to the search and rescue team at the site of the accident. “We were fortunate that our dealers, who are also expert operators of the DTG2, were there to help,” said Amanda Coulas, Project Manager at Deep Trekker.

The DTG2 is a lightweight (8.5 kg) and portable remotely operated vehicle (ROV) designed for underwater exploration. It uses a patented internal pitching system that allows the ROV to move horizontally and vertically using only two thrusters. The independently mounted camera allows the operator to either set the camera to hold steady as the body rotates, or set the camera to move with the body as it pitches, making it possible to do detailed inspections. The DTG2’s power is contained within the submersible, and its low-drag tether has a diameter of just 4mm, allowing it to maneuver with minimal impact from the surface. Its die-cast aluminum casing is anodized and powder coated, making it suitable for both fresh and salt water use.

Until recently, most underwater ROVs have been too expensive and complicated to be easily deployed by rescue crew – many costing in the range of $25K – $50K and requiring skilled engineers to operate. But according to Macdonald, the search and rescue market is starting to realize that it can have an ROV solution within their budget. The Deep Trekker unit operating at the wreckage site in South Korea runs under $5000 USD and their DTG2 Worker model, which is equipped with a two-function grabber and allows operators to retrieve objects under water, is priced just under $8000 USD. Deep Trekker has ROVs working in 34 countries at present.

“In general, it only takes a few minutes to understand the controls, an hour or so to be good at them and maybe a weekend of practice to be an expert operator,” said Coulas. “Other ROVs require topside power – a generator – to send power down the tether to operate the ROV. This makes it much harder to transport the ROV in a zodiac or small rescue boat. Our ROV can operate 5-8 hours on one 3-hour charge.”

A Waterloo-based company, Deep Trekker got its start in the Great Lakes region of Ontario, Canada. “My partner and the engineering brains behind Deep Trekker Inc., Jeff Lotz, had developed an ROV for a college project about 14 years ago after he capsized his canoe and lost a lot of gear,” said Macdonald via email. “I had a similar situation in 2009 when I dropped a flashlight off of my boat, and began thinking about all the exploration that could be done in the Great Lakes if we had an ROV. Through a series of connections, the three partners in Deep Trekker Inc. got together and began exploring the possibility of an affordable, portable ROV that would be accessible to everyone. After meeting, conceptualizing and building for about 18 months, the first Deep Trekker voyage was made in Lake Huron.” The trio’s website was later discovered by an aquaculture equipment dealer in Norway, whose initial purchase of several of units helped to launched the company.

Search and rescue is a relatively new market for Deep Trekker, which in the past has concentrated on the aquaculture, commercial diving, recreation, and energy sectors. A Research and Markets report released in March estimates the global ROV market to be $1.2 billion in 2014, primarily driven by offshore drilling.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development.This video shows a prototype of the “Jeff” autonomous underwater vehicle (AUV).

This video shows a prototype of the “Jeff” autonomous underwater vehicle (AUV) with high maneuverability for exploring underwater habitat, and the ability to dock to the base station on the water surface. The video also shows a swarm of “Lily” AUVs making the largest autonomous underwater swarm in the world.

To learn more about the project, see this introductory post, or check out all the videos from the Year of CoCoRo on Robohub.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. In this video, the CoCoRo autonomous swarm finds a target collectively by using a bio-inspired algorithm.

To learn more about the project, see this introductory post, or check out all the videos from the Year of CoCoRo on Robohub.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. This video shows a swarm of “Lily” AUVs that are able to measure the swarm size by using a bio-inspred algorithm.

To learn more about the project, see this introductory post, or check out all the videos from the Year of CoCoRo on Robohub.

The EU-funded Collective Cognitive Robotics (CoCoRo) project has built a swarm of 41 autonomous underwater vehicles (AUVs) that show collective cognition. Throughout 2015 – The Year of CoCoRo – we will be uploading a new weekly video detailing the latest stage in its development. In this video a lure AUV that is controlled by a human steers an autonomous swarm towards a desired goal.

To learn more about the project, see this introductory post, or check out all the videos from the Year of CoCoRo on Robohub.