Adapting to performance variations in multi-robot coverage

This paper proposes a new approach for a group of robots carrying out a collaborative task to adapt on-line to actuation performance variations among the robots. We consider the problem of multi-robot coverage, where a group of robots has to spread out to cover the environment. We suppose that some robots have poor actuation performance (e.g. weak motors, friction losses in the gear train, wheel slip, etc.) and some have strong actuation performance (powerful motors, little friction, favorable terrain, etc.). The robots do not know before hand the relative strengths of their actuation compared to the others in the team. The algorithm in this paper learns the relative actuation performance variations among the robots on-line, in a distributed fashion, and automatically compensates by giving the weak robots a small portion of the environment, and giving the strong robots a larger portion. Using a Lyapunov-type proof, we prove that the robots converge to locally optimal positions for coverage. The algorithm is demonstrated in both Matlab simulations and experiments using Pololu m3pi robots.