Efficiency-Aware Cooperative Path Planning for Truck–UAV Power Line Inspection

Abstract

Vehicle-mounted unmanned aerial vehicle (UAV) cooperative inspection has emerged as an efficient approach for power line inspection and has gained attention for its ability to monitor transmission lines in great detail. Although existing truck–UAV path planning studies commonly optimize overall completion time or travel distance, synchronization losses arising from truck–UAV interactions are rarely modeled explicitly, limiting the direct improvement of coordination efficiency. To address this issue, a coordination-efficiency-aware path planning method is developed for truck–UAV power line inspection. First, a cumulative truck waiting loss is introduced to quantify the efficiency degradation caused by temporal mismatches between trucks and UAVs. Based on this metric, a joint optimization model is formulated by integrating truck routing, UAV task assignment, launch and recovery decisions, and inspection sequencing. To solve the problem efficiently, a two-stage solution framework is further developed, in which truck skeleton route construction is combined with UAV cooperative embedding to decompose the joint optimization problem. Results show that the proposed method improves inspection efficiency compared with the truck-only strategy. By introducing UAV-assisted inspection, the system makespan is reduced from 150.023 s under the truck-only strategy to 87.983 s under the No-reorder strategy. Furthermore, compared with the No-reorder strategy, the proposed Stage-2A strategy further reduces the system makespan to 87.350 s and decreases the cumulative truck waiting loss from 42.629 s to 39.542 s. These results demonstrate that optimizing UAV intra-sortie sequences improves truck–UAV temporal coordination and reduces coordination losses. Compared with Greedy, Nearest Neighbor (NN), and Genetic Algorithm (GA), Stage-2A achieves lower system makespan and waiting losses across different node scales.