# Topics > Multi-systems > Swarm >  CoCoRo (Collective Cognitive Robots), swarm of autonomous underwater vehicles, Artificial Life Laboratory, University of Graz, Graz, Austria

## Airicist

Artificial Life Laboratory

Project Leader - Thomas Schmickl

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## Airicist

CoCoRo Largest AUV swarm 2013 

 Published on May 29, 2013




> This video shows the CoCoRo swarm the largest autonomous swarm of underwater robots in the world.
> The swarm is capable of collective behaviours such as cooperative search, finding the most inetersting place of the habitat, and making collective decisions.

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## Airicist

Short CoCoRo AUV swarm underwater video

 Published on Jul 16, 2014




> CoCoRo project Lily AUVs filmed with an underwater camera (Sony AS100V) in a pool.

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## Airicist

CoCoRo CeBIT 

 Published on Oct 8, 2014




> This video shows the CoCoRo autonomous underwater vehicles (AUVs) in a large aquarium at the CeBIT exhibition.
> 
> This experimental setup 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.
> ...

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## Airicist

Overview Jeff robot

Published on Jan 4, 2015




> The Year of CoCoRo 01/52: 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.

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## Airicist

Overbiew CoCoRo largest swarm 

Published on Jan 11, 2015




> The Year of CoCoRo 02/52: The CoCoRo system is currently the largest autonomous underwater swarm in the world. This video briefly shows some of its components and functions.

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## Airicist

TYOC#03/52 Jeff Massive Exploration 

Published on Jan 18, 2015




> The Year of CoCoRo 03/52: A swarm of Jeff robots is browsing the environment. They use their front blue-light sensors to detect and avoid obstacles.

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## Airicist

TYOC#04/52 Aggregation Parcour 

Published on Jan 25, 2015




> The Year of CoCoRo 04/52: In a complex underwater habitat a swarm of Jeff robots first searches for a magnetic target. Then the communicate to Lily robots at smaller depths to join the group.

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## Airicist

TYOC#05/52: Lily SwarmSizeAwareness 

Published on Feb 8, 2015




> The Year of CoCoRo 05/52: 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.

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## Airicist

TYOC#06/52: Jeff SwarmSizeMeasurement 

Published on Feb 8, 2015




> The Year of CoCoRo 06/52: 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.

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## Airicist

TYOC#07/52: Lily Flocking by slimemold 

Published on Feb 15, 2015




> The Year of CoCoRo 07/52: 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.

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## Airicist

TYOC#08/52: Lily Emergent Taxis 

Published on Feb 22, 2015




> The Year of CoCoRo 08/52: A swarm of Lily robots can form a coherent group by exchanging light pulses in a slime-mold inspired way among the group members. 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.

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## Airicist

TYOC#09/52: Jeff in the water current 

Published on Mar 1, 2015




> The Year of CoCoRo 09/52: 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).

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## Airicist

TYOC#10/52: Feeding Jeff with magnets 

Published on Mar 8, 2015




> The Year of CoCoRo 10/52: 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 :-)

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## Airicist

TYOC#11/52: Livorno Out Of Lab 

Published on Mar 30, 2015




> The Year of CoCoRo 11/52: This video summarizes all out-of-the-lab activities in which we tested our robots (Lily and Jeff). 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).

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## Airicist

TYOC#12/52: Fun with Jeff in the pool 

Published on Mar 22, 2015




> The Year of CoCoRo 12/52: 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 & 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.

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## Airicist

TYOC#13/52 Lilycam in Nature EXTRA 

Published on Mar 29, 2015




> The Year of CoCoRo 13/52: Lily is an autonomously diving robot. After we attached an underwater camera onto of 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 forrests“, fish and other water organisms.

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## Airicist

TYOC#14/52: LilyCam in a Lake EXTRA 

Published on Apr 5, 2015




> The Year of CoCoRo 14/52: 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.

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## Airicist

TYOC#15/52 TRAILER LilyCam in a wild river 

Published on Apr 12, 2015




> The Year of CoCoRo 15/52: There are not only beautiful lakes in Austria. There are also 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 downhill 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 to see such movies ;-)

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## Airicist

TYOC#16/52: JeffCam in a Lake 

Published on Apr 19, 2015




> The Year of CoCoRo 16/52: 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.

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## Airicist

TYOC#17/52: Lily Confinement by Bluelight 

Published on Apr 26, 2015




> The Year of CoCoRo 17/52: 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.

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## Airicist

TYOC#18/52: Jeff Confinement ElectricField 

Published on May 3, 2015




> The Year of CoCoRo 18/52: 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.

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## Airicist

TYOC#19/52: Confinement Livorno 

Published on May 8, 2015




> The Year of CoCoRo 19/52: After having tested the electric-field confinement of Jeff robots to the base station in our pool, we went out to Livorno harbor 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.

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## Airicist

TYOC#20/52: Jeff Autonomous Docking

Published on May 17, 2015




> The Year of CoCoRo 20/52: 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. First, this functionality was tested with a fixed mounted docking device, then with a docking device floating around in our pool. To say it short: Autonomous docking worked exceptionally well under all conditions.

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## Airicist

TYOC#21/52: Jeff Docking Livorno

Published on May 24, 2015




> The Year of CoCoRo 21/52: After successful test of autonomous docking in our pool, we tested this functionality also in Livorno harbor. Despite all the waves, turbulences, wind, rain and other challenges the docking worked very reliable and fast. We could also use the docking to deploy a robot to specific sites with our radio-controlled surface station. Keep in mind: the docking and undocking is autonomous, as is also the Jeff robot as soon as it undocks from the base station.

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## Airicist

TYOC#22/52: Aggregation Magnets

Published on May 31, 2015




> The Year of CoCoRo 22/52: In this experiment a swarm of Lily robots has to find a magnetic target on the ground. This is made difficult by water currents that make single robots drift away after they have found the target. By recruiting other robots to this location with blue-light blinks a larger group can be formed at the target site. This stabilizes the swarm at that location, thus preventing the unwanted drifting away. In addition, several weak magnetic field spots (local optima) make it even more difficult for the swarm to find and aggregate at the right (strong) magnetic target spot. Collectively the robots manage to solve the task following a very simple behavioral program and a 1-bit communication principle. This shows that very simple agents can manage to perform complex tasks together without any global knowledge available to them.

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## Airicist

TYOC#23/52: Lily Aggregation Experiments

Published on Jun 7, 2015




> The Year of CoCoRo 23/52: Based on the algorithm we have presented last week in "The Year of CoCoRo", we conducted a series of collective choice experiments with our Lily swarm robots in two distinct environments: Open field, which asked for choosing a global magnetic optimum over several local ones, and a T-Maze setup, which was offering 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.

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## Airicist

TYOC#24/52: Jeff Exploration

Published on Jun 14, 2015




> The Year of CoCoRo 24/52: This video shows Jeff robots in searching a structured, highly fragmented habitat. 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 already quickly, more robots even faster. This scalability is important to generate a useful robot swarm.

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## Airicist

TYOC#25/52: JEFFCAM in Pool

Published on Jun 21, 2015




> The Year of CoCoRo 25/52: We put an underwater camera on one of our JEFF robots diving autonomously in a structured, in fact highly fragmented habitat. This way we want to see how 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. Later, as we have shown already in previous weeks, we did extend this approach also to natural habitats (lakes and an ocean harbor). In the video shown here now, we tested the areal coverage of the swarm and how many interesting sites will be caught on camera.

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## Airicist

TYOC#26/52: Magnetic Target 4Compartments

Published on Jun 28, 2015




> The Year of CoCoRo 26/52: We constructed a fragmented habitat in a pool providing 4 compartments separated by medium-height walls. One compartment contained a magnetic/metallic object acting as a search target. One JEFF robot was introduced performing a random-walk-based search pattern occasionally interrupted by a "jump-over-the-wall" behavior to allow it to switch compartment after a collision event by blue-light LEDs was detected. The autonomous robot observed its internal magnetic sensor to identify the target in case it is nearby. In this case, it stops there, sinks on top of the target and switches on its LEDs to mark it. The video shows a series of experiments to see how many successful results we can achieve in a row and how often "false positives" happen. We were quite surprised about the reliability of the behavior given the fact that the robot is only driving randomly (no specific optimized search pattern!) and no higher-cognitive capabilities (no map, no camera, no memory, no plan!) are involved.

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## Airicist

TYOC#27/52: Jeff target find large pool

Published on Jul 5, 2015




> The Year of CoCoRo 27/52: After we tested our search algorithm for magnetic targets in the small pool (see previous movie from last week) we went out to a pool having several times this water volume. We used 4 Jeff robots this time, performing a random-walk based search program. Although we had many trouble in this long-night experiment we achieved some success in this extend search scenario. We had mostly software-problems that made the robots run too much along cyclic trajectories instead of doing an efficient random walk. This was the stuff we had to fight with in early summer 2014, later throughout the year we made significant improvements, as later videos will show. Although the target-finding rate was not perfect, the robots still found the target several times and it was the first time we operated our robots successfully at water depths below 2m.

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## Airicist

TYOC#28/52: All Magnetfinding

Published on Jul 12, 2015




> The Year of CoCoRo 28/52: This video shows our test of "dead reckoning" performed by a Jeff robot in Livorno harbor. The robot was programmed in a way that it goes along a straight-line trajectory for some time, then performs a circular search pattern (including collision avoidance) and then, after some time, makes a direct return to the point where it was started. The robot only uses its internal magnetic compass sensor, its internal clock and blue-light LEDs for collision avoidance. No global information like GPS was involved, the robot had no absolute positioning system. We were surprised how well this very simple behavior worked despite the currents in the harbor and despite occasional collision-avoidance behaviors triggered by buoys in the harbor. The safety line (a thin fishing thread), which we applied to make sure the robot does not get lost, was actually never needed as the robot came back to us like a boomerang every time.

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## Airicist

TYOC#29/52: Jeff Preprogrammed Trajectory

Published on Jul 19, 2015




> The Year of CoCoRo 29/52: This video shows our test of "dead reckoning" performed by a Jeff robot in Livorno harbor. The robot was programmed in a way that it goes along a straight-line trajectory for some time, then performs a circular search pattern (including collision avoidance) and then, after some time, makes a direct return to the point where it was started. The robot only uses its internal magnetic compass sensor, its internal clock and blue-light LEDs for collision avoidance. No global information like GPS was involved, the robot had no absolute positioning system. We were surprised how well this very simple behavior worked despite the currents in the harbor and despite occasional collision-avoidance behaviors triggered by buoys in the harbor. The safety line (a thin fishing thread), which we applied to make sure the robot does not get lost, was actually never needed as the robot came back to us like a boomerang every time.

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## Airicist

TYOC#30a/52: ANIMATION Combined Scenario No1

Published on Jul 26, 2015




> The Year of CoCoRo 30a/52: In CoCoRo the search for a sunken metallic object on the ground of the waterbody was dealt with in several scenarios. The most complex one was scenario #1 which consists of the following phases: (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, 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 this place by blue-light LED signals, leading to a positive feedback loop aggregating more and more Jeff robots at this place. (4) In the meanwhile the Lily robots swarm out, but not as a swarm, they rather build a chain that ultimately connects the surface station to the aggregation of 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 to human operators) and vice versa. This scenario #1 combines many of the individual algorithms and functionalities that were shown in the previous videos of "The Year of CoCoRo". This video is a computer animation illustrating the phases of our "scenario #1".

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## Airicist

TYOC#31/52: Combined Scenario No1, Basestation Arrival

Published on Aug 2, 2015




> The Year of CoCoRo 31/52: The first phase of our "combined scenario #1" is that the CoCoRo system, as a whole, enters the habitat. Then happens the only human intervention in the execution of the scenario: The swarm gets released by pressing a button performed by a human operator. This sends a WLAN signal that triggers the release of the swarm: For this very important security measure we kept the human in the loop on purpose, 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 ground of the water body and start their search for the metallic/magnetic target object while the Lily robots swarm out to later build the "relay chain" between the target spot and the surface station.

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## Airicist

TYOC#32/52: Combined Scenario No1, Targetfind

Published on Aug 9, 2015




> The Year of CoCoRo 32/52: Another important phase of the "combined scenario #1" in CoCoRo is the collective search performed by Jeff robots on the ground of the water body for a sunken metallic/magnetic target object. We conducted an extra series of experiments on this phase of our "combined scenario #1" to see the efficiency and robustness of this autonomous swarming behavior. It is based on correlated random walk with collision avoidance and observation of the internal compass of the robot. After finding the object other robots are recruited to this place by horizontal blue-light signals to ultimately achieve an aggregation of Jeff robots around the target that marks this location for the Lily robots which will then form the "relay chain" to the surface station.

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## Airicist

TYOC#33/52: Combined 1 relay chain

Published on Aug 16, 2015




> The Year of CoCoRo 33/52: In the CoCoRo system there is often a distant gap between the swarm of Jeff robots on the ground that found a search target and the base station at the water surface. Underwater communication is often difficult and limited. To bridge this gap in communication a swarm of Lily robots build a chain, which we call a "relay chain", as it serves as a relay that allow the ground swarm and the surface station to communicate with each other through light pulses, RF or electric pulses. Here we show several instances of such a relay chain in the middle phase of our "combined scenario #1" and 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 by executing a special "shoaling algorithm" which is inspired by fish shoaling behavior. It is based on reactions to 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/orient relative to the neighboring configuration. Thus the relay chain formation itself is almost communication-free, as the photodiodes and blinks could be easily replaced by a video camera and object detection. It is just passive sensing and interpretation of the local environment. The only communication act performed is the relaying of the RF pulse information, which was implemented similar to slime-mould amoebas' communication strategy.

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## Airicist

TYOC#34/52: Relay Chain Basics

Published on Aug 23, 2015




> The Year of CoCoRo 34/52: This video shows a series of experiments we performed on the building and persistency of the relay chain, which is 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 of experiments they are moving, thus the relay chain formed by the robot swarm has to reconfigure and reshape. The robots forming the chain perform an algorithm inspired by the shoaling behavior of fish. They look out for blue-light blinks of neighboring robots with their photoreceptors (photodiodes). From the light intensity that falls into those sensors they infer very roughly the distance and angle of those neighbors. Based on these pieces of information they decide into which direction they turn and move. Although the mechanism involves blinking signals the algorithm can work "communication free", as an onboard camera and image processing can replace this sensing and then no signal has to be send from neighboring AUVs. Thus it can also operate on passive sensing, what is important for the scalability of the method.

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## Airicist

TYOC#35/52: RelayChain Communication

Published on Aug 30, 2015




> The Year of CoCoRo 35/52: In this video we show 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 such a pulse will also send a similar pulse, this way they relay the signal along the chain. To have control over the directionality of the signal spreading, 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.

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## Airicist

TYOC#36a/52: ANIMATION Relayswarm

Published on Sep 6, 2015




> The Year of CoCoRo 36A/52: The "combined scenario #1", which we demonstrated in last 4 weeks on The Year of CoCoRo in detail, uses a relay chain of robots to communicate between the sea ground and the surface station. This worked well but we also investigated alternatives 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 week we show again 2 videos. This video here explains the scenario in a computer animation, the other video shows the scenario in real-world experiments.






TYOC#36b/52: CoCoRo RelaySwarm

Published on Sep 6, 2015




> The Year of CoCoRo 36B/52: This 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, it spreads like in 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, ultimately make the Jeff robot to go up to the surface above the found target.

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## Airicist

TYOC#37a/52: ANIMATION Combined Scenario 2

Published on Sep 13, 2015




> The Year of CoCoRo 37A/52: Again, we bring 2 videos online this week on "The Year of CoCoRo". This video shows an explanatory computer animation of our "combined scenario #2", which is again a variant of a collective search scenario. In a fragmented habitat only one compartment holds the magnetic search target. In every compartment a swarm of Jeff robots searches the ground. The Jeff robots that find this target inform Lily robots that spread this information into other compartments by moving randomly. As soon as Jeff robots, that did not find search targets in their current compartment so far, are informed that other Jeff robots somewhere else have found something, they rise up to the surface, make a random walk and then sink down to the ground again into a randomly chosen new compartment. Over time the whole swarm of robots converges to the one place where the target was found. By signaling upwards by Jeff and Lily robots, also the surface station is attracted to the place above the search target.






TYOC#37b/52: Combined No2 All In One

Published on Sep 13, 2015




> The Year of CoCoRo 37B/52: This video shows several runs of the "combined scenario #2", which is a collective search scenario. In a fragmented habitat a swarm of Jeff robots (on the ground) and Lily robots (information carriers at all depths) and a simple surface station -- we used a special Lily robot as a surrogate here -- cooperate to identify the compartment of the habitat that holds the magnetic search target. The robots perform the algorithms/behaviors described in the computer animation we also published this week (movie 37A/52 on The Year of CoCoRo). Initially each one of the four environmental compartments holds one searching Jeff robot on the ground. At the end of the run, three of these robots are located in the compartment with the target, many Lily robots and also the surrogate of the surface station are aggregated there. The one Jeff robot that did not reach the target compartment was informed about the finding of the other Jeff robots but for mechanical reasons it could not change its depth to overcome the compartmental wall, although it tried several times, as indicated by the green LED signals it shows from time to time. This is how it works in swarm robotics. There are always some robots not performing well. The good news is that this is part of the game in a swarm system: They stay functional as a collective even though some of the swarm members malfunction.

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## Airicist

TYOC#38/52: CoCoRo 2013 BEECLUST

Published on Sep 20, 2015




> The Year of CoCoRo 38/52: After we focussed in the last 2 months on showing quite complicated search scenarios we will now focus for some weeks on the topic of "collective awareness" and "collective decision making". In this early laboratory experiment shown here we implemented the BEECLUST algorithm in a swarm of Lily robots. The BEECLUST is a simple swarm algorithm derived from the walking and resting behavior of young honeybees which can compare several temperature spots in their environment and collectively choose the optimal (warmest) spot. Thus the group is aware of all options in its environment and chooses the best one without having every single individual agent knowing all available options. In the example we show in the video here the algorithm was "translated" from honeybees to underwater robots as follows: All robots move randomly in the habitat. When they meet another robot, and only then, they measure how deep the water below them is. The more shallow it is, the longer they stay in place. By running this algorithm, the swarm is able to identify shallow places and collectively choose the shallowest of all. Of course the same can be done to find the deepest point, the darkest or the brightest point in the habitat, it is only one more step necessary: correlating the resting time of the robots with other local environmental properties. The BEECLUST is one of the simplest swarm algorithms possible, maybe it is even THE simplest algorithm that you can do and still call it a "swarm algorithm". Simplicity is very nice, as it allows easy translation from one domain into another domain. However, an algorithm like this does not suit for every application, as we had to learn: 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 due to water turbulences. Even in an aquarium this is already an issue if many robots move around and induce such turbulences. The algorithm relies on this rest-in-place behavior, so we concluded that for a more turbulent underwater habitat there have to be better algorithms than the classical BEECLUST.






Swarm Robot Aggregation (BEECLUST algorithm)

Uploaded on Oct 7, 2008




> Robots aggregate using simplistic algorithms.






Robot swarm performing the BEECLUST algorithm

Uploaded on Nov 25, 2011




> Time lapse recording (15 x) of a swarm of 15 Jasmine robots employing the bio inspired BEECLUST algorithm, navigating through a dynamic complex light gradient in an arena (150 x 100 cm). The lighting is controlled by two lamps above the left and right edge of the arena. The experiment is divided into four phases with different lighting conditions, each of which lasting for 3 min in realtime (12 s in time lapse): 1: left lamp off, right lamp dim; 2: left lamp bright, right lamp dim; 3: left lamp dim, right lamp bright; 4: left lamp dim, right lamp off. When one lamp is off, the dim lamp appears as bright as the bright lamp when the other lamp is dim. This is a camera artifact which does not affect the robots. If a robot detects another robot at a range of 5 cm or less, it stops and rests for a period proportional to the local light intensity. This behaviour is inspired by honeybees, which aggregate at optimal areas using a comparable mechanism with local temperature as a clue. When available, bright areas induce larger aggregations of robots. Dim areas are less attractive and only induce small aggregations, especially when competing with a bright area.


BEECLUST: A Swarm Algorithm Derived from Honeybees. Derivation of the Algorithm, Analysis by Mathematical Models and Implementation on a Robot Swarm

by Thomas Schmickl, Heiko Hamann

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## Airicist

TYOC#39/52: Lily AdaptiveLayers pool

Published on Sep 27, 2015




> The Year of CoCoRo 39/52: This video shows a novel swarm algorithm called "social inhibition algorithm". It is inspired by honeybees which use mechanisms to regulate division of labour by socially inhibiting physiological age progression: The more bees of an older age group meet other younger bees, the slower these younger bees will progress in their age-depending work schedule. And the more often younger bees meet older bees the faster these older bees will progress, thus they will age faster physiologically. This way the physiological age is self-regulated in the honeybee colony, ultimately also regulating the division of labour this way. We implemented these mechanisms into our robot swarm as follows: Each robot holds a variable X representing the "physiological age" of the robot. If two robots meet, these values can change a bit, depending on the robots past experiences. Over time all robots will have quite different values of X. The work to be performed, or in our case here the depth at which the robot dives, is based on its value of X. If robots are removed or added in specific depths, the contact rates of some robots will change, in turn also their values of X will change, ultimately re-arranging the whole swarm in a homeostatic way. By casting lights at some robots we increase the "work load demand" at this depth layer and automatically more robots are recruited to that layer. The video shows our first experiments of this algorithm in a pool and having the robots at 2 different depths: near the surface and close to the ground.

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## Airicist

TYOC#40/52: Lily AdaptiveLayers watercolum

Published on Oct 4, 2015




> The Year of CoCoRo 40/52: Last week we have already 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 video this week explains the mechanisms used in the algorithm in more detail and shows our experiments with swarms of robots in a vertical aquarium. The robots regulate an internal variable X via interactions with other robots and split the swarm into 3 different cohorts allocating themselves at different depths in the aquarium. We add and remove robots to the swarm in the aquarium and their regulation of the internal variable X leads to automatic rearrangement in the swarm such that there is always an equal number of robots at each depth. We also add sources of interest which are special robots or external lights at different depth layers and the swarm rearranges in a way that more robots are attracted in these especially 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 interesting for being used in large scale scenarios where several sub-groups of the swarm are needed to act in different depth levels performing different tasks within the swarm.

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## Airicist

TYOC#41/52 Jeff metacognition

Published on Oct 11, 2015




> The Year of CoCoRo 41/52: In previous videos we have already demonstrated the ability of a swarm of Jeff robots to find a magnetic target. Now we offer the swarm two different targets of different quality, e.g. because the targets are of different 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 to measure the sizes of local robot groups with just a 1-bit blinking signal. By combining both algorithms of target search and of swarm-size measurement we now 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 meta-cognition of the swarm: The swarm collectively knows whether or not it knows what the best magnetic target site is.

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## Airicist

TYOC#42/52: Murky waters

Published on Oct 18, 2015




> The Year of CoCoRo 42/52: This video shows a collection of algorithms and communication channels that we explored for coordination of robots in intransparent, murky, turbid waters. The individual algorithms have already been shown in larger settings on Lily and/or Jeff robots in the Year of CoCoRo. However, in this compilation we clearly demonstrate the appropriateness of the algorithms for very murky waters by doing most experiments in milk. Yes, milk. We only used a very small pool, as we wanted not to (ab)use too many valuable natural resources. First we were wondering whether or not our robots can also operate in milk. They can. We think these were the worldwide first milk-swarm-robotic experiments, but we are not sure about that, there are other crazy people out there, so you can never know :-)

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## Airicist

TYOC#43/52 Channel switching

Published on Oct 25, 2015




> The Year of CoCoRo 43/52: 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. However, also those channels can be disturbed or blocked, as there are natural magnetic fields, natural electrical charges and WiFi is also easily disturbed. So it is crucial for the swarm to dynamically choose the appropriate communication channel. This short video shows an on-table experiment in which two Jeff robots dynamically choose between blue-light and RF communication. Of course one can just always send and receive with all channels, but this might just jam the channels on the one hand and maximizes the waste of energy (battery time) on the other hand. So a dynamic choice of just one channel is a preferential setup and our CoCoRo robots are capable of doing this.

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## Airicist

TYOC#44/52 Lily Shoaling 

Published on Nov 1, 2015




> The Year of CoCoRo 44/52: For a swarm it is crucial that the robots stay together. One method is to generate a "virtual fence" around the swarm, as we already demonstrated it with various "confinement" videos in the Year of CoCoRo (weeks 17-19). Besides those methods it is also nice if the robots stay together like a school of fish, a flock of birds or a swarm of mosquitos would do it. We wanted to mimic those simple individual behaviors that produce collectively a coherent global swarming behavior. Our Lily robots look at blinking blue-light signals form neighboring robots. Based on the estimated distance between the robots they decide with a simple threshold-based mechanisms in 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.

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## Airicist

TYOC#45/52: SwarmControl

Published on Nov 8, 2015




> The Year of CoCoRo 45/52: This week we demonstrate that our swarms are autonomously shoaling on the one hand, but still can be controlled from the outside on the other hand: Groups of Lily robots emit blue light blinking signals that can be seen by other nearby robots. Those robots can estimate their distance and angle to other robots this way and autonomously relocate to stay together. And if just one of the robots is radio-controlled then the human operator can in fact influence (and thus control) the whole swarm. However, the operator cannot just move straightly to the target. The operator has to observe the swarm and also react to it. Or as Tobias, who was doing this in this movie, called it: "You have to feel the swarm".

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## Airicist

TYOC#46/52: JeffShoaling

Published on Nov 15, 2015




> The Year of CoCoRo 46/52: The body shape of Jeff robots is much closer to fish than the shape of Lily robots. With their slim bodies, Jeff robots could 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 is not working in 100% of the time but very often. And when we filmed the little fish that observed our experiments with robots in Livorno harbor (see at beginning of the movie) the natural fish also did not align for 100% of the time. So we came pretty close to them. We implemented this code only in a very short period of time (hours!) towards the end of the project, thus we think that with more time and a bit more of 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.

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## Airicist

TYOC#47/52: CEBIT

Published on Nov 22, 2015




> The Year of CoCoRo 47/52: In March 2014, we exhibited CoCoRo at the CEBIT in Hannover, Germany. This is Europe's largest consumer electronics fair. First we thought we might be too far off-topic and all the latest news on flatscreen TVs, smartphones and gaming consoles will just overshadow our exhibition. We were very wrong: We had the smallest booth and were overrun with thousands of people throughout the week. TV teams visited us and radio interviews followed each other. Our own internal estimate concluded that we might have had the highest rate of visitors per square meter in the whole fair. It is not easy to bring a swarm of underwater robots into such a fair and to show them running in live experiments. But we managed to do it and it was a huge thing for us, we still talk about those days often. Thanks to our enduring team members and also to all the people who visited us. The project gained a lot of motivation from the public interest we felt there.

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## Airicist

TYOC#48/52: Workshops

Published on Nov 29, 2015




> The Year of CoCoRo 48/52: During the runtime of the project we held a high number of workshops. These workshops were a driving force for the whole project because each international partner team had to bring their work in time to those workshops and we all together had to implement mechanical hardware, electronics and software into working installations. Almost all videos shown here in The Year of CoCoRo originate from such workshops, which were always focussing on one or several specific demonstrators. This form of workshop-driven development proved to be very successful, as at the end of the project we were able to show 17 working final demonstrators with our swarm. This allowed to demonstrate the versatility of robot swarms, as we demonstrate it also here in The Year of CoCoRo.

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## Airicist

TYOC#49/52: OVERVIEW Basestations of CoCoRo

Published on Dec 6, 2015




> The Year of CoCoRo 49/52: During the last year we showed many swarm algorithms in various experiments. The spotlights were always on the Lily and on the Jeff robots. However, there is another star in the team and this trailer is dedicated to this special agent: The base station! It was finished rather at the end of the project thus we had to develop (hack) many surrogates and placeholders for it over the course of the project. We got so experienced with it that we could quickly hack together a surrogate base station from almost everything that was laying around in the lab: Styrofoam, cans, boxes, ... whatever was around and came in handy. This video shows some of those creations. Finally, 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): Fast as hell, highly maneuverable, just elegant. The base station has a docking device and can actively maneuver, dock and undock robots and carry three attached spare robots with it. With this central masterpiece we were ready for our final review.

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## Airicist

TYOC#50/52: Overview Scalability

Published on Dec 13, 2015




> The Year of CoCoRo 50/52: With the research project CoCoRo we went a long way. We started with "pools" with a size of a few cubic centimeters and with naked electronics on the table. Throughout 3.5 years we extended the size scale and the number of robots steadily. We went through aquariums, pools of various sizes, threw our robots into ponds, rivers and lakes and finally ended our experiments in salt water in the basin of the harbor of Livorno. In retrospective, our number of robots increased by a factor of 40 and the volume of the waterbody we tested them in increased by a factor of 40 million. Quite some stretch for a small project. With our new project subCULTron, which extends the work of CoCoRo, we again scale up the swarm size (120+ robots) and the habitat size (Lagoon of Venice).

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## Airicist

TYOC#51/52: CoCoRo Big Vision

Published on Dec 20, 2015




> The Year of CoCoRo 51/52: This video shows the initial "big vision" trailer that we produced at the beginning of the project. It shows the basic components of the robotic system that we targeted (surface station, relay chain, ground swarm) and shows how we imagined our collective of underwater robots forming coherent swarms. The video was important for us to stay focussed in the project as it ensured that all project partners shared the same target goal for their work. The video is based on a small simulator, written by us to produce this video. However the simulator grew into a much more important tool: During the project this initial simulator was extended into a very useful simulator (including underwater physics with fluid dynamics) for simulating underwater swarms. It was used in evolutionary computation to find good shoaling behavior for our robots. Next week we will publish our final video in "THE YEAR OF COCORO" to show the whole system implemented on real underwater robots in a comparable setting and to show how our initial vision became reality.

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## Airicist

TYOC#52/52: Final demonstrator

Published on Dec 27, 2015




> The Year of CoCoRo 52/52: CoCoRo - The final goals were achieved. This video shows our final demonstrator as it was presented to our reviewers which then granted the CoCoRo project the grade "EXCELLENT" in their final assessment. This video shows the whole CoCoRo system working together: A base station, a "relay-chain" swarm and a search swarm on the ground. All of them communicating and interacting with each other.

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