基于因果影响检测的多无人机海上协同导航策略优化方法

Multi-UAV cooperative navigation is a crucial technology for achieving efficient cooperative maritime operations. However, in vast and dynamically unknown maritime environments, limited sensing capabilities and autonomous decision-making mechanism lead to complex cooperation relationships among UAVs, making it difficult to obtain global information. In recent years, multi-agent reinforcement learning under the centralized training and decentralized execution paradigm has achieved remarkable progress in learning cooperative behaviors and has been widely applied to cooperative maritime navigation tasks. Nevertheless, because agent interactions often occur only in specific situations, improving cooperation efficiency and exploration capability remains a major challenge. To address this issue, this paper proposes a causal influence detection for multi-agent proximal policy optimization method. The proposed method uses causal influence among agents as an evaluation metric and introduces an intrinsic reward mechanism designed based on cooperation rules. By leveraging causal inference and conditional mutual information, the method detects behavioral causal influence among agents, guiding them to preferentially explore actions that positively affect the global state and thus enhancing inter-agent cooperation. Experimental results demonstrate that the proposed method achieves significant performance improvements, especially in maritime search and rescue tasks, where it exhibits higher cooperation efficiency, validating the effectiveness of the method.