An Improved Wall-Climbing Robot Complete Coverage Path Planning Method for Wind Turbine Blade

In wind turbine blade maintenance, path planning for wall-climbing robots is a critical issue. The large size and complex curvature variations of turbine blades pose unique challenges to path planning. To address this problem, this study proposes an energy-consumption-based bio-inspired neural network method for complete coverage path planning. By considering the robot's energy consumption, this method better integrates height differences, angle differences, and distance differences between grid cells, optimizing the robot's energy expenditure on the map. The simulation results show that the algorithm can effectively handles the size and curvature variations of wind turbine blades, providing an efficient and reliable solution for the path planning of wall-climbing robots.