Intelligent Inspection Route Planning Based On the Improved Ant-Lion Optimization Algorithm

The challenges of autonomous and joint inspection of intelligent equipment, such as Unmanned Aerial Vehicles (UAVs) and robots, in substations are constantly gaining attention. To efficiently capture high-quality images of power equipment, we propose a method for automatically generating joint inspection points for multiple intelligent equipment. It introduces a shooting potential model and a spatial relationship model to assess the suitability of shooting power equipment at different spatial points using various intelligent equipment. A point selection method is designed based on shooting quality constraints and spatial constraints to optimize inspection efficiency. The focus is on generating non-redundant and complete inspection points that are suitable for multiple intelligent equipment to perform with. Additionally, we study cooperative path planning of the heterogeneous robot systems to reduce the time required for the inspection task. An improved Ant-Lion Optimization Algorithm is proposed to decouple the routes of mobile robots and UAVs and plan their routes simultaneously. The effectiveness of the proposed method is demonstrated on a virtual substation experimental platform, revealing improvements in shooting quality, inspection efficiency, and reduced inspection time.