A Path Planning Algorithm of Inspection Robots for Solar Power Plants Based on Improved RRT

In order to improve the safety and efficiency of inspection robots for solar power plants, the Rapidly Exploring Random Tree Star (RRT*) algorithm is studied and an improved method based on an adaptive target bias and heuristic circular sampling is proposed. Firstly, in response to the problem of slow search speed caused by random samplings in the traditional RRT* algorithm, an adaptive target bias function is applied to adjust the generation of sampling points in real-time, which continuously expands the random tree towards the target point. Secondly, to solve the problem that the RRT* algorithm has a low search efficiency and stability in narrow and long channels of solar power plants, the strategy of heuristic circular sampling combined with directional deviation is designed to resample nodes located on obstacles to generate more valid nodes. Then, considering the turning range of the inspection robot, our method will prune nodes on the paths that fail to meet constraint of the minimum turning radius. Finally, the B-spline curve is used to fit and smooth the path. A simulation experiment based on the environment of solar power plant is conducted and the result demonstrates that, compared with the RRT*, the improved RRT* algorithm reduces the search time, iterations, and path cost by 62.06%, 45.17%, and 1.6%, respectively, which provides a theoretical basis for improving the operational efficiency of inspection robots for solar power plants.

Automated summary

This study aims to improve the safety and efficiency of inspection robots for solar power plants. Through the improvement of the RRT* algorithm, a method based on adaptive target bias and heuristic circular sampling is proposed. The experimental results show that the improved algorithm significantly reduces search time, iterations, and path cost, providing a theoretical basis for enhancing the operational efficiency of inspection robots for solar power plants.