Reinforcement Learning-Based Low-Altitude Path Planning for UAS Swarm in Diverse Threat Environments

Abstract

Unmanned aircraft systems (UASs) with autonomous maneuvering decision capabilities are expected to play a key role in future unmanned systems applications. While reinforcement learning has proven successful in solving UAS path planning problems in simple urban environments, it remains under-researched for some complex mountain environments. In this paper, the path planning of UAS swarm for the low-altitude rapid traverse in diverse environments is studied when facing the threats of complex terrain, radars and swarm failure. First, a UAS swarm radar detection probability is built up for evaluating the radar detection threat by a networked radar system, where the detection probability of a UAS swarm is equated to a single UAS with appropriate position and radar cross section named as the swarm virtual leader. Second, a reinforcement learning based path planning method is proposed to seek the optimal path for the swarm virtual leader which balances instantaneous reward, including detection probability and path constraints with terminal reward, including normal rate. Third, a formation optimization strategy is designed to further reduce the threat of radar detection through dynamically adjusting the formation geometry. Final, simulations in the complex environment have been carried out to evaluate the performance of the proposed method, where the path quality, task success rate and normal rate are counted as the performance indicators.