Decentralized prioritized motion planning for multiple autonomous UAVs in 3D polygonal obstacle environments

Abstract

This paper presents a decentralized multi-agent motion planning method for aerial robots moving among 3D polygonal obstacles. The algorithm combines a prioritized A* algorithm for high-level (global) planning along with a barrier functions-based method for low-level (local) coordination and control. We first extend the barriers function method developed in earlier work to treat arbitrary polygonal obstacles. We then combine the prioritized A* algorithm to compute waypoints and paths that facilitate the performance of the barrier-based coordination and collision avoidance. We assume that the obstacles are known to the agents, and that each agent knows the state of other agents lying in its sensing area. Simulation and experimental results with quadrotors in 2D and 3D environments demonstrate the efficacy of the proposed approach.