Motivated by the strengths of unmanned aerial vehicle (UAV), the UAV–human collaboration route planning (UHCRP) for intelligent infrastructure inspection is a problem worthy of discussion to help reduce human costs and minimize the risk of noninspected infrastructures under limited resources. To facilitate UHCRP, this paper proposes a novel deep reinforcement learning (DRL)‐based approach to well handle multi‐source uncertain features and constraints at a fast speed. To begin with, UHCRP is mathematically described and reformulated as a dual interdependent deep reinforcement learning (diDRL) framework to reflect real‐world scenarios. Afterward, a novel policy network named the attention‐based deep neural network (A‐DNN) is introduced to learn the route planning decisions for the combinatorial optimization problem. In particular, A‐DNN is made up of an encoder and a dual decoder for UAV and human inspection, where the multi‐head attention mechanism is incorporated to generate richer representations for model performance improvement. Performance of the proposed dual multi‐head attention model (DAM) has been tested in simulations and a real‐world case study regarding wind farm inspection. Results indicate that DAM under the sampling decoding strategy can deliver a high‐quality path plan and show better generalizability for larger scale problem sizes compared to single‐head attention model (SAM), multi‐head attention model (AM), and two baseline models, namely OR‐Tools and genetic algorithm. Moreover, DAM trained by randomly generated data can be directly employed to solve the practical problem with standardization of inputs. Overall, DRL integrates decision‐making for inspection method selection and inspected infrastructure selection, providing adaptive and intelligent inspection path planning for UAV and human in complex and dynamic engineering environments.

中文翻译：

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无人机-人类协作智能基础设施巡检路径规划

受无人机（UAV）优势的推动，用于智能基础设施巡检的无人机-人类协作路线规划（UHCRP）是一个值得讨论的问题，有助于降低人力成本，并最大限度地减少资源有限的情况下未巡检基础设施的风险。为了促进 UHCRP，本文提出了一种基于深度强化学习（DRL）的新颖方法，可以快速处理多源不确定特征和约束。首先，UHCRP 经过数学描述和重新表述为双重相互依赖的深度强化学习 (diDRL) 框架，以反映现实世界的场景。随后，引入了一种名为基于注意力的深度神经网络（A-DNN）的新型策略网络来学习组合优化问题的路线规划决策。特别是，A-DNN 由用于无人机和人类检查的编码器和双解码器组成，其中结合了多头注意力机制来生成更丰富的表示以提高模型性能。所提出的双多头注意力模型（DAM）的性能已经在模拟和有关风电场检查的实际案例研究中进行了测试。结果表明，与单头注意力模型（SAM）、多头注意力模型（AM）和两个基线模型相比，采样解码策略下的 DAM 可以提供高质量的路径规划，并且对于更大规模的问题表现出更好的泛化性，即 OR-Tools 和遗传算法。此外，通过随机生成的数据训练的DAM可以直接用于解决输入标准化的实际问题。总体而言，DRL集成了检测方法选择和被检测基础设施选择的决策，为复杂动态的工程环境中的无人机和人类提供自适应和智能的检测路径规划。