# Laser Ablation Manipulator Coverage Path Planning Method Based on an Improved Ant Colony Algorithm
###### Abstract
More importantly, using point cloud data does not require environmental segmentation--data simplification is sufficient. Thus, the adaptability of this method is improved, which implies that the method can be used for CPP on complex surfaces, closed surfaces, and at the intersection of multiple surfaces. Using point cloud data lays the foundation for transforming the path-planning problem into an optimisation problem, thereby enabling this method to solve both the problems of CPP and point-to-point path planning. Laser processing is characterised by a variable laser spot size. Another advantage of using the point cloud data is that it can work well with this characteristic. To ensure machining quality, people always expect that the curvature within the same laser spot range only has a small change. A larger spot size can be used to improve the efficiency at a position with a small curvature change. In contrast, a smaller spot size must be used to meet the machining requirements at a position with a large curvature change. After the point cloud is simplified, this problem can be formulated as a travelling salesman problem (TSP), which is an NP (non-deterministic polynomial)-hard problem. However, only a few scholars have conducted research based on this view. [PERSON] 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. However, this is similar to the sequence optimisation problem mentioned above. As the path of a single subregion needs to be adjusted multiple times, the algorithm complexity increases. [PERSON] et al. [27] proposed a method to solve the CPP problem using multiple drones. The path planning of subregions is considered as a TSP, and the energy consumption is added to the optimisation goal. However, this method has been tested only in relatively simple environments. Due to the similarity between CPP and TSP, in this study, an improved ant colony algorithm was used to solve the CPP problem to obtain an optimal path. Moreover, the trajectory optimisation information is added to obtain a more practical path for optimisation objectives because trajectory planning is often required after path planning in engineering practice. Simultaneously, the path smoothing is also achieved because the acceleration factor is added to the optimisation objective. On the other hand, using the inverse kinematics solution can transform the Cartesian space path planning into joint space trajectory planning. Thus, the manipulator can avoid losing control in the singular position because it is replaced once there exists a singular position. The remainder of the article is structured as follows. In Section 2, the point cloud processing method is proposed. In Section 3, the fitness function is proposed. The improvement measures of the ant colony algorithm and the process of path planning are proposed in Section 4. Section 5 provides an analysis of the effectiveness of the proposed method using the simulation results. The conclusions are presented in Section 6. The content and function of each section are shown in Table 1.