Research on Pursuit-evasion games with multiple heterogeneous pursuers and a high speed evader

We deal with Pursuit-evasion games with a high speed evader which has superiority in velocity over a group of heterogeneous pursuers in this paper. Heterogeneity in the group of pursuers is expressed as heterogeneity in the individual maximum speeds. We introduce Apollonius circles generated by every pursuer and the evader to analyse the criteria for a successful capture. Collective robots pursuit problem under unknown environment is investigated from the view of behavior-based control method, and Motor Schema-based reactive control architecture is adopted. Three basic behaviors using for pursuers are designed, namely Move_to_goal, Avoid_obatacle and Hunting, wherein hunting behavior is realized through a kind of reinforcement learning algorithm called Q-Learning. Pursuit of the evader is based on synthesized behavior, generated through summarizing the outputs of all behaviors weighed. Results of simulation experiments validate the effectiveness of our method.