Abstract
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.
Highlights
Laser ablation has a wide range of applications in engineering [1]
This paper mainly focuses on the path planning methods of laser ablation
The point cloud is a dataset of points that have been used widely to represent shapes since the pioneering work of Alexa et al [28]
Summary
Laser ablation has a wide range of applications in engineering [1]. This paper mainly focuses on the path planning methods of laser ablation. Three main types of methods exist for sequence optimisation: greedy algorithms [20], dynamic programming [21], and evolutionary algorithms [22] The purpose of this step is to determine the order in which different subregions are traversed and adjust the coverage path of each subregion if necessary. Xie et al [26] developed two approaches to solve the CPP problem among multiple spatially distributed regions by considering the path planning in a single subregion as a CPP problem and the path planning between subregions as a TSP This is similar to the sequence optimisation problem mentioned above. The path planning of subregions is considered as a TSP, and the energy consumption is added to the optimisation goal This method has been tested only in relatively simple environments.
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