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Thread: CoCoRo (Collective Cognitive Robots), swarm of autonomous underwater vehicles, Artificial Life Laboratory, University of Graz, Graz, Austria

  1. #21


    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.

  2. #22


    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.

  3. #23


    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.

  4. #24


    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.

  5. #25


    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.

  6. #26


    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.

  7. #27


    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.

  8. #28


    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.

  9. #29


    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.

  10. #30


    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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