ノイズ耐性通信と注意機構を備えたMARLによる堅牢なマルチUAV協調
Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention Mechanisms
COMAをベースに注意機構付き通信プロトコルを組み込み、ノイズ環境下でも複数UAVの経路計画を堅牢かつ効率的に行うマルチエージェント強化学習フレームワークを提案した。
著者: Zilin Zhao, Chishui Chen, Haotian Shi, Jiale Chen, Xuanlin Yue, Zhejian Yang, Yang Liu
分類: cs.MA, cs.AI, cs.RO
原文アブストラクト
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.
