Aiming at the problem that traditional heuristic algorithm is difficult to extract the empirical model in time from large sample terrain data, a multi-UAV collaborative path planning method based on attention reinforcement learning is proposed. The method draws on a combined consideration of influencing factors, such as survival probability, path length, and load balancing and endurance constraints, and works as a support system for multimachine collaborative optimizing. The attention neural network is used to generate the cooperative reconnaissance strategy of the UAV, and a large amount of simulation data is tested to optimize the attention network using the REINFORCE algorithm. Experimental results show that the proposed method is effective in solving the multi-UAV path planning issue with high real-time requirements, and the solving time is less than the traditional algorithms.

中文翻译：

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基于注意力机制的多无人机协同路径规划方法

针对传统启发式算法难以从大样本地形数据中及时提取经验模型的问题，提出一种基于注意力强化学习的多无人机协同路径规划方法。该方法综合考虑生存概率、路径长度、负载均衡和耐力约束等影响因素，作为多机协同优化的支撑系统。利用注意力神经网络生成无人机的协同侦察策略，并通过大量仿真数据进行测试，利用REINFORCE算法优化注意力网络。实验结果表明，该方法能有效解决实时性要求高的多无人机路径规划问题，