多无人机协同路径规划在噪声环境下更稳健，通过注意力机制提升通信与决策能力。
Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention Mechanisms
- 基于COMA的多智能体强化学习框架，引入注意力通信协议。
- 在噪声环境中路径规划效率提升，熵减少78%。
- 适合复杂3D场景下多无人机协同任务，如遥感信息采集。
无人飞行器（UAV）的高效路径规划在远程感知和信息收集中至关重要。随着任务规模扩大，多UAV协同部署显著提升了信息收集效率。然而，在路径规划中，多UAV的协作通信与决策仍是主要挑战，尤其在噪声环境下的表现堪忧。为在三维空间中高效完成复杂信息收集任务并解决通信鲁棒性问题，我们提出一种基于反事实多智能体策略梯度（COMA）算法的多智能体强化学习（MARL）框架。该框架融合基于注意力机制的UAV通信协议与训练-部署系统，显著提升了噪声条件下的通信鲁棒性和个体决策能力。在合成数据集与真实数据集上的实验表明，该方法在路径规划效率与鲁棒性方面均优于现有算法，特别是在噪声环境中，熵减少了78%。
原文摘要 · Abstract (English)
Efficient path planning for unmanned aerial vehicles (UAVs) is crucial in remote sensing and information collection. As task scales expand, the cooperative deployment of multiple UAVs significantly improves information collection efficiency. However, collaborative communication and decision-making for multiple UAVs remain major challenges in path planning, especially in noisy environments. To efficiently accomplish complex information collection tasks in 3D space and address robust communication issues, we propose a multi-agent reinforcement learning (MARL) framework for UAV path planning based on the Counterfactual Multi-Agent Policy Gradients (COMA) algorithm. The framework incorporates attention mechanism-based UAV communication protocol and training-deployment system, significantly improving communication robustness and individual decision-making capabilities in noisy conditions. Experiments conducted on both synthetic and real-world datasets demonstrate that our method outperforms existing algorithms in terms of path planning efficiency and robustness, especially in noisy environments, achieving a 78\% improvement in entropy reduction.
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