Multi-UAV Collaborative Path Planning Method Fusing Multi-Head Attention and SAC

Abstract

Aiming at the problem of low convergence efficiency of traditional multi-UAV path planning algorithms in unknown complex environments, this paper proposes a deep reinforcement learning algorithm incorporating the attention mechanism. The method is based on the Soft Actor-Critic (SAC) framework, which introduces a multi-attention mechanism in the Critic network, dynamically learns the dependency relationship between intelligences, and realizes key information screening and conflict avoidance. An environment with multiple random obstacles is designed to simulate complex emergent situations. The results show that the proposed algorithm significantly improves the mission success rate and average reward, significantly extends the survival time and exploration range of the UAVs, and verifies the effectiveness of the attention mechanism in enhancing the efficiency, robustness, and long-term planning capability of multi-UAV collaboration, as compared to the baseline method that does not use attention.