Path Planning for Detection Robot Climbing on Rotor Blade Surfaces of Wind Turbine Based on Neural Network

Two-feet climbing robot is proposed for rotor blade surface damage detection. The penalty function is designed based on the simulated annealing neural network for waypoints inside blade. According to the derivative of path energy function to time, waypoints are updated and move toward the direction reducing the path energy consisting of length and penalty function. According to the curvature variation range, a novel weighted simulated-annealing-temperature updating method is designed to get comprehensive optimization of the path energy and convergence speed. The path planning is accomplished for the root, middle, and tip blade parts, respectively. The asymptotic analysis of the waypoint coordinating updating process was given, and the updated start point is adopted during escaping from inside. The effect of weight on path energy and convergence speed is analyzed. The planning results show the effectiveness of the proposed path planning algorithm, and the weighted average method is valid for the comprehensive optimization.