# Laser Ablation Manipulator Coverage Path Planning Method Based on an Improved Ant Colony Algorithm
###### Abstract
Metaheuristic methods are more popular and widely used for the path planning of mobile robots [7]. Coverage path planning (CPP) is the task of determining a path that passes over all points of an area or volume while avoiding obstacles [8]. This task is integral to many robotic applications, such as agriculture and farming robots [5], cleaning robots [9], lawn mowers [10], autonomous underwater vehicles [11], unmanned aerial vehicles [12], and demining robots [13]. Generally, coverage algorithms are categorised as offline and online algorithms [14]. Offline coverage algorithms use fixed information, and the environment information is known in advance. In contrast, online algorithms do not assume full prior knowledge of the environment to be covered and utilise real-time sensor measurements to sweep the target space [15]. Furthermore, [PERSON] et al. [16] summarised the coverage methods in three steps. The first step is to divide the environment into smaller regions (cells) for effective coverage. Then, the path planning of each subregion and the sequence planning among the subregions are performed according to the sweep direction. The last step is path smoothing. There are several environmental decomposition methods, including cell-based decomposition techniques [17], grid-based methods [18], and spanning-tree-based coverage methods [19]. All these methods are based on the main concept of dividing the environment into different regions, similar to a regular region, to facilitate subsequent path planning. Regular paths are often used to cover each subregion, such as a spiral path [10] and a boustrophedon path [17]. 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. Two path-smoothing method classes exist, namely, the graphical method [23] and the function-based method [24]. The main aim of path smoothing is to avoid having the robots stop, slow down, or reorient at sharper turns because path-planning algorithms, such as A *, D *, PRM, and RRT *, generate rectilinear (linear piecewise) paths for robots [25]. The graphical method uses simple shapes, such as circles and arcs, to generate a smooth path. In the function-based method, the path trajectory is represented by function equations, such as spirals and spline-based functions. Many researchers have discussed the CPP problem. However, the state of the art needs to be significantly improved, particularly in terms of accuracy, efficiency, robustness, and optimisation [15]. The core idea of the above methods is to use regular curves or broken lines to traverse the fixed area, without considering many optimisation conditions. Typically, only the shortest route length or the smallest number of turns is considered. More importantly, because the global path is divided into multiple parts, the results of current methods are the superposition of multiple local optimal solutions, and not the global optimal solution. Additionally, most methods only consider the problem of covering a continuous planar area. Such methods cannot deal with CPP on complex surfaces, closed surfaces, and intersections of multiple surfaces. These methods can be applied to CPP for cleaning robots, demining robots, and unmanned aerial vehicles, but not for machining robots. To overcome the shortcomings of existing methods, in this study, the problem of path planning was examined from a different perspective. The primary purpose of path planning is to determine quickly the obstacle-free path that meets the requirements. Therefore, the path-planning process must include environmental modelling, optimisation objectives and boundary conditions, and optimisation methods. In this study, point cloud data were used to model the working environment to improve the flexibility of planning, because point cloud data can be obtained in real time or in advance.