Evolutionary robotics applied to the multi-robot worst-case pursuit-evasion problem

This paper proposes the use of an evolutionary robotics approach to solve the worst-case pursuit-evasion problem, in which evaders are considered arbitrarily fast and omniscient, while pursuers have limited sensing and communication capabilities, with no previous knowledge of the environment. Unlike most work in evolutionary robotics, we offer a control system for multiple mobile robots based on finite state machines derived using a genetic algorithm. Results show that, given a sufficient number of robots, the evolved system is capable of clearing a discrete map, including multiply connected maps, of all previous contamination.