In 2016, the European Union co-funded 17 new robotics projects from the Horizon 2020 Framework Programme for research and innovation. 16 of these resulted from the robotics work programme, and 1 project resulted from the Societal Challenges part of Horizon 2020. The robotics work programme implements the robotics strategy developed by SPARC, the Public-Private Partnership for Robotics in Europe (see the Strategic Research Agenda). 

Every week, euRobotics will publish a video interview with a project, so that you can find out more about their activities. This week features Co4Robots: Achieving Complex Collaborative Missions via Decentralized Control and Coordination of Interacting Robots.

Objectives

Recent applications necessitate coordination of different robots. Current practice is mainly based on offline, centralized planning and tasks are fulfilled in a predefined manner. Co4Robots’ goal is to build a systematic methodology to:

• accomplish complex task specifications given to a team of potentially heterogeneous robots;
• develop control schemes appropriate for mobility and manipulation capabilities of the robots;
• achieve perceptual capabilities that enable robots to localize themselves and estimate the dynamic environment state;
• integrate all in a decentralized framework.

Expected impact

1. The envisioned scenarios involve multi-robot services in e.g. office environments. Although public facilities are in some degree pre-structured, the need for the Co4Robots’ framework is evident since:
2. it will lead to an improved use of resources and a faster accomplishment of tasks inside workspaces with high social activity;
3. it will contribute towards the vision of more flexible multi-robot applications in both professional and domestic environments, also in view of the “Industry 4.0” vision and the general need to deploy such systems in everyday life scenarios.

Partners

KTH ROYAL INSTITUTE OF TECHNOLOGY
BOSCH
NATIONAL TECHNICAL UNIVERSITY OF ATHENS
PAL ROBOTICS
FOUNDATION FOR RESEARCH AND TECHNOLOGY HELLAS
UNIVERSITY OF GOTHENBURG

Coordinator:Prof. Dimos Dimarogonas
dimos@kth.se
www.facebook.com/co4robots

Project website: www.co4robots.eu

Watch all EU-projects videos

If you enjoyed reading this article, you may also want to read:

• New Horizon 2020 robotics projects, 2016: An.Dy
• New Horizon 2020 robotics projects, 2016: BADGER
• Two Horizon 2020 projects researching EU Digital Industrial Platform for Robotics
• EU’s Horizon 2020 has funded $179 million in robotics PPPs

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  :-)

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 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.

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

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• Thousand-robot swarm self-assembles into arbitrary shapes
• 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.

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 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.