Wind Farm Cabin Track Robot Inspection Path Based on Intelligent Algorithm

With the continuous expansion of wind farm scale, traditional manual inspection can hardly meet the demand, and robot inspection has become an inevitable trend. This paper proposes an adaptive dynamic ant colony optimization algorithm (ADACO) for wind farm cabin track robot inspection path planning. By deeply analyzing the dynamic change characteristics of the wind farm environment, the pheromone update mechanism and path selection strategy are innovated on the basis of the traditional ant colony algorithm. The experiment sets up a variety of scenarios, including normal operation, equipment failure, and obstacles on the track. The results show that in complex working conditions, the inspection time of the ADACO algorithm is shortened by 20% compared with the basic ant colony algorithm, and the equipment coverage rate is increased by 15%; the path length is shortened by 10% on average compared with the genetic algorithm, and the convergence speed is faster. This fully proves that the ADACO algorithm has significant advantages in improving inspection efficiency, optimizing paths, and enhancing equipment coverage, providing an effective solution for intelligent inspection of wind farms.