Heuristics for the Multi-Robot Worst-Case Pursuit-Evasion Problem

There is a growing demand for the use of robots to assist humans in their tasks, especially those involving risks, such as search and rescue. For this reason, coordination among several robots has been a common option, and one of the ways to study and model these applications involves the problem of pursuit evasion. This paper extends the results presented earlier on 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 prior knowledge regarding environments, which are treated as discrete and can be multiply connected. First, a formulation based on random walk is offered. Then, the concept is extended to include a decentralized multi-robot control system based on a finite-state machine with state-action mapping defined by means of a genetic algorithm. Results show that the proposed system is able to decontaminate several types of maps, but does not generalize to all initial conditions, due to the incompleteness in the automaton mapping. Therefore, a complementary approach is presented in which random walk is used alternatively with the evolved automaton, indicating random actions in cases of states not sufficiently visited during evolution. In addition, a comparative analysis of the evolutionary approach and the random walk formulation is also carried out.