Distributed Pursuit of an Evader With Perception and Localization Uncertainty for Autonomous Aerial Vehicles in Obstacle Environments

This article address the pursuit–evasion problem of autonomous aerial vehicles (AAVs) in cluttered environments with perception and localization uncertainty. To generate trajectories that avoid collisions during the pursuit process, we propose the chance-constraint pursuit Voronoi cell (CCPVC), featuring AAV-to-AAV and AAV-to-obstacle separation hyperplanes. We develop an optimization problem to determine these hyperplanes and offer a corresponding solution technique. In addition, by incorporating a buffer term, CCPVC exhibits favorable probabilistic collision avoidance properties. Leveraging CCPVC, an algorithm for nearest point identification in pursuit contexts and a distributed model predictive control framework are introduced for AAV trajectory planning. We provide proofs of the probabilistic collision avoidance properties ensured by CCPVC and the distributed control input calculation strategy. Finally, the viability and effectiveness of the proposed methodology is demonstrated through simulation and experimental validation.