针对电力巡检场景的多无人机任务分配与路径规划问题，提出了一种最大航程约束的多无人机任务分配与路径规划算法。首先基于市场拍卖的方法引入了跳点搜索算法（jump point search，JPS）算法预估无人机到巡检任务以及巡检任务到巡检任务之间的最短避障路径长度，有效解决了任务分配和路径规划的耦合问题；其次，在优化目标函数中融合了无人机的最大航程约束，确保无人机可以在其最大续航能力内完成所分配到的巡检任务；最后，提出了一种懒惰的拍卖策略，在保证任务分配求解质量不变的情况下进一步提高了算法求解的收敛速度。实验结果表明，提出的方法相比于现有方法更符合续航能力有限的旋翼无人机作业需求，同时求解任务分配的时间最大减少了近40%。实验结果证明了所提方法的实用性和有效性。

A novel task allocation and path planning algorithm with maximum endurance constraints is proposed for the problem of multi-UAV task allocation and path planning in electric power inspection. Firstly, the jump point search （JPS) algorithm is introduced based on the auction mechanism to estimate the shortest obstacle avoidance path lengths between UAVs and tasks and between tasks and tasks, effectively addressing the coupling problem of task allocation and path planning. Secondly, the maximum endurance constraint of UAVs is integrated into the optimization objective function to ensure that UAVs can complete the assigned inspection tasks within their maximum endurance capacities. Finally, a lazy auction strategy is proposed to speed up the algorithm's convergence without compromising the solution quality of task allocation. The simulations show that the proposed method is more suitable for the operational requirements of multirotor UAVs with limited endurance than existing methods. Moreover, the time needed to solve the task allocation is reduced by nearly 40%. The experiments demonstrate the practicality and effectiveness of the proposed method.