Arc Routing for Drone Road Inspection with Dynamic Battery Swapping: A Case Study of Heshan City

Electric drones have been widely applied in highway infrastructure inspection as a low-carbon maintenance solution, but the limited battery endurance severely restricts large-scale continuous operations. While ground vehicles as mobile battery swapping platforms can alleviate this issue, they bring challenges including multi-mode flight selection for drones and complex spatiotemporal coupling constraints between drones and vehicles. This study focuses on the Drone Road Inspection Arc Routing Problem (ARP) with dynamic battery swapping. With the goal of minimizing total inspection time, a mixed-integer programming model integrating a three-layer network structure and virtual node extension is established. Through rational selection of drone flight modes and coordinated planning of vehicle–drone paths, the non-inspection flight time of drones is effectively reduced, which also helps cut unnecessary energy consumption. To address the challenge of solving large-scale instances, an Adaptive Large Neighborhood Search (ALNS) Algorithm with battery endurance-oriented operators is proposed. Numerical experiments show that the algorithm exhibits great performance in both solution efficiency and quality. The case study of Heshan City verifies that the proposed hybrid flight strategy delivers notable benefits in both path optimization and energy consumption reduction, and the sensitivity analysis clarifies the optimization directions of key parameters, providing technical reference for sustainable road maintenance.