Coverage path planning on a complex free-form surface is a representative problem that has been steadily investigated in path planning and automatic control. However, most methods do not consider many optimisation conditions and cannot deal with complex surfaces, closed surfaces, and the intersection of multiple surfaces. In this study, a novel and efficient coverage path-planning method is proposed that considers trajectory optimisation information and uses point cloud data for environmental modelling. First, the point cloud data are denoised and simplified. Then, the path points are converted into the rotation angle of each joint of the manipulator. A mathematical model dedicated to energy consumption, processing time, and path smoothness as optimisation objectives is developed, and an improved ant colony algorithm is used to solve this problem. Two measures are proposed to prevent the algorithm from being trapped in a local optimum, thereby improving the global search ability of the algorithm. The standard test results indicate that the improved algorithm performs better than the ant colony algorithm and the max–min ant system. The numerical simulation results reveal that compared with the point cloud slicing technique, the proposed method can obtain a more efficient path. The laser ablation de-rusting experiment results specify the utility of the proposed approach.
A Framework for Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection
