Joint Nest Deployment and Path Planning for Multi-U AV Power Inspection

Unmanned Aerial Vehicles (UAVs)-based intelligent inspection is considered a promising technology for future inspection systems. In this paper, we study a multi-UAV autonomous power inspection system, where a set of UAVs fly towards the towers to take photos, i.e., collecting data on insulators. We provide a joint design aiming to minimize nest construction cost and total inspection time by jointly optimizing nest deploying and UAVs path while satisfying the constraint of maximum UAV flight time. However, the formulated problem is a multi-objective coupled problem, and the algorithm's complexity increases hierarchically with the number of towers. To solve such issues, we first decompose the problem into a nest deployment sub-problem and a UAVs path planning sub-problem. Then a joint optimization scheme is proposed, including a sparrow search K-means (SS-Kmeans) algorithm for nest deployment and an adaptive pheromone concentration ant colony (APCAC) algorithm for UAVs path planning. Finally, simulation results show that the proposed algorithm can effectively balance the relationship between UAV inspection efficiency and cost, and the performance of the proposed method significantly outperforms the baselines.