In our final video we demonstrate the entire CoCoRo system communicating and interacting together, including: the base station, a “relay-chain” swarm, and a search swarm on the ground.

The video was our demonstration to our reviewers. I’m pleased to report the CoCoRo project received an ‘Excellent’ in their assessment!

The Year of CoCoRo has now concluded; we hope you had fun watching the project videos throughout the year. CoCoRo will live on in the follow-up project: subCULTron. You can learn more about subCULTron on our website, twitter, or Facebook.

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 – uploaded new weekly videos detailing the latest stage in its development.

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.

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.

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.

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 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.

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

• 3’600-strong underwater robot sensor network returns 1’000’000th measurement, has scientists craving for more
• 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

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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

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• 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 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. This video shows the “Lily” AUVs staying together and moving as a shoal using a communication-less 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. 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.