Abstract: Unmanned aerial vehicle (UAV) has been widely used to replace the traditional manual inspection due to its safety, flexibility, and efficiency. However, the existing vehicle-mounted UAV and the support vehicle face many difficulties in electric inspection and with an excessive cost and a low efficiency. In this paper, the vehicle-mounted UAV cooperative operation problem (VUCOP) for transmission inspection is studied in detail by proposing an integrated vehicle-mounted UAV cooperative operation model based on the space-time network. To ensure the efficiency and accuracy of the solution approach, the Lagrangian relaxation based algorithm is proposed embedded with the greedy-rule based feasibility approach. The performance of the algorithm proposed is verified through a series of larger scale problems. Eventually, experiments of sensitive analysis are conducted to investigate the effect of the UAV battery capacity on optimal system operation strategy. This paper provides a scientific decision-making basis for improving the operation efficiency of the transmission inspection system of the vehicle-mounted UAV and reducing the management cost, and provides a novel optimization approach for effectively solving the problem of VUCOP.

Key words: transmission inspection, vehicle-mounted UAV, cooperative operation optimization, space-time network, Lagrangian relaxation algorithm

摘要： 无人机以其安全、灵活和高效的特点已被广泛用于替代传统的人工巡检，但现有的车载无人机和保障车在电力巡检协同作业中面临重重困难，巡检成本较高且效率较低。针对车载无人机电力巡检的协同作业优化问题，构建基于时空网络的无人机与保障车时空路径协同优化模型；以拉格朗日松弛为主要框架，嵌入基于贪心规则的松弛解转换算法，提高模型求解的收敛速度和精度，经数值实验对比验证了该优化方法在求解较大规模问题时的有效性。此外，还通过敏感性分析实验研究无人机电池最大载荷对系统整体运行状态的影响。本研究为提升车载无人机电力巡检系统运行效率和降低管理成本提供科学的决策依据，为有效解决车载无人机电力巡检协同作业难题提供新方法。

关键词: 电力巡检, 车载无人机, 协同作业优化, 时空网络, 拉格朗日松弛算法