Adaptive strategy selection for multi-robot search based on local communication and sensing

This paper presents a simulation model for experimenting with locally adaptive movement strategies for robots involved in collective robotic search tasks in rapidly changing and uncertain environments. The model assumes that the nature of the environment restricts inter-robot communication and uses a form of stigmergy based local communication which has been widely applied in collective robots. The model is based on a biased random walk where the degree of bias is linked to a local control variable which can change depending on the evaluation of local adaption strategies. The local adaption strategies use an approach based on activation functions to control the choice of which candidate paths should be inhibited or have increased preference over random motion. Experiments aim to test the effectiveness of this approach for optimal collective search in various test domains. A series of initial experiments is presented demontrating aspects of the model.