Intelligent pursuit & evasion in an unknown environment

This paper introduces a novel and flexible simulation platform for studying pursuit and evasion in unknown 2-D environments of arbitrary obstacles, in an effort to expand the practical application of pursuit-evasion research. The platform provides realistic simulation of the sensing capability of each robotic agent (either a pursuer or an evader). Each agent uses real-time local sensing to collect information from the environment while it simultaneously plans and executes its motion to best satisfy one or more objectives. The evader´s objectives are to reach a specific goal location as quickly as possible and to avoid being caught by the pursuer. The pursuer´s objectives are to locate and capture the evader whose motion is unknown, and when the evader is not seen, explore the environment and predict where the evader may be. Under a common real-time planning paradigm, each agent´s planner dynamically adapts its goals and objectives to the agent´s changing circumstances so that the agent can always choose the best course of action. Simulation results have shown that the introduced approach is an effective means to study sophisticated pursuit-evasion scenarios and accomplish objectives for both the pursuer and the evader in an unknown environment. The platform can be easily expanded to accommodate multiple agents in more complex pursuit-evasion tasks.