Dynamic RCS-Based Deep Reinforcement Learning Path Planning for Fixed-Wing UAVs in Complex Environments
TL;DR
A deep reinforcement learning-based path planning framework is developed, in which the Soft Actor-Critic (SAC) algorithm is employed to solve the penetration task under dynamic RCS constraints, with SAC achieving the shortest mean penetration time among successful trials.
Abstract
This paper investigates the low-altitude penetration path planning problem for a fixed-wing UAVs in complex battlefield environments with mountainous terrain and radar threats. First, a radar detection probability model and a dynamic radar cross section (RCS) model are established to characterize target exposure risk. Then, a deep reinforcement learning-based (DRL) path planning framework is developed, in which the Soft Actor-Critic (SAC) algorithm is employed to solve the penetration task under dynamic RCS constraints. A multi-objective reward design is constructed to jointly account for target-reaching progress, radar-threat avoidance, obstacle avoidance, and terminal task completion. Simulations show dynamically feasible flight paths under the adopted models. HIL tests conducted in the corresponding five-radar and two-radar numerical scenarios support onboard path planning, waypoint transmission, and functional closed-loop execution. Comparisons reveal a trade-off among penetration time, modeled radar exposure, and computation, with SAC achieving the shortest mean penetration time among successful trials.