Robot swarms are known to be robust to individual robot failures. However, a reduced swarm size causes a reduced swarm density. A~too low swarm density may then decrease swarm performance, which should be compensated by individual behaviour daptation. Similarly, swarm behaviors can also be adapted to changes in the environment, for example, due to dynamic light conditions. We study aggregation of swarm robots controlled by an extended variant of the BEECLUST algorithm. The robots are asked to aggregate at the brightest spot in their environment. Our approach efficiently adapts this swarm aggregation behavior to variability in swarm density and light conditions. Each robot monitors its environment continuously by sampling its local swarm density and light condition. The robots share features of these measurements with their neighbors by communication. In a set of 25~swarm robot experiments with dynamic light conditions and a few also with dynamic swarm density, we find an improved performance compared to robot swarms without communication and without awareness of the swarm density.
Collective Adaptivity to Dynamic Swarm Densities and Light Conditions in Robot Swarms
2018-09-14
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
DDC: | 629 |
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