Controlling Heterogeneous Swarms through Minimal Communication Between Homogeneous Sub-swarms
Carlo Pinciroli, Rehan O'Grady, Anders L. Christensen, Marco Dorigo
Submitted to ANTS 2010

[ Abstract ] [ Video ] [ Contact ]


  Abstract


In swarm robotics, the agents are often assumed to be identical. In this paper, we argue that the cooperation between swarms of different kinds of robots can enhance the capabilities of the robotic system --- heterogeneous swarms marry the robustness and parallelism of homogeneous swarms with efficient task specialisation. A key problem of heterogeneous swarm systems is the potential complexity of inter-agent communication between different robot types. We show that minimal communication between homogeneous sub-swarms is sufficient to engineer ordered global behaviours. To test our approach, we run simulated experiments in which a group of flying robots recruits and delivers wheeled robots to target locations.



  Video



In this video, tasks are activated in sequence. An eye-bot requests 5 to 10 robots to execute the task it is coordinating. The request is relayed to the closest eye-bot in the recruitment area, which takes care of recruiting the needed foot-bots. When the team is formed, the recruiting eye-bot delivers it to the requesting eye-bot. After the execution of the task, the foot-bots are returned to the recruitment area. At this point, another eye-bot requests foot-bots for its task (9 to 13) and also in this case recruitment, delivery and return are successful. This is footage from one of the experimental trials described in our paper.

In this video, we show that the recruitment system is successful also when dealing with multiple parallel and asynchronous requests. Initially, two eye-bots request foot-bots at the same time. One eye-bot requests 5 to 10 foot-bots, the other 7 to 13. The requests are relayed to two eye-bots in the recruitment area. While the two foot-bot teams are formed in parallel, a third eye-bot requests 10 to 12 foot-bots. This new request triggers the redistribution of the already recruited foot-bots. Eventually, one team is formed and, when the team leaves the recruitment area, further redistribution takes place, thus allowing another group to be formed and sent to task execution. The third team is formed when the first is returned to the recruitment area. This is footage from one of the experimental trials described in our paper.

In this video, we show how deadlocks are solved in the system. There are 30 available foot-bots in the recruitment area and four simultaneous recruitment requests (min=12, max=13) are formulated at the same time. The eye-bots form their teams in parallel, but soon a deadlock happens -- no eye-bot can satisfy the minimum requested quota. When eye-bots detect convergence to a quota which is less than the minimum, it has a small probability to spike the leaving probability sent to the foot-bots. This simple mechanism is sufficient to allow the system to overcome the deadlock and continue functioning. This is footage from one of the experimental trials described in our paper.




  Contact


Webpages:

Swarm-bots: www.swarm-bots.org
Swarmanoid: www.swarmanoid.org

Address:

IRIDIA - ULB
50 Avenue F. Roosevelt - CP 194/9
1050 Bruxelles
Belgium