Laser Ablation Manipulator Coverage Path Planning Method Based on an Improved Ant Colony Algorithm

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.

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

“…Gao et al [242] proposed an improved ACO algorithm to optimize the coverage performance by reducing the number of turns in multi-robot CPP in simulated 2D grid space. Ye et al [12] improved the algorithm by randomly calculating the transition probability and updating the pheromone besides the acceleration factor, improving the global searchability despite the randomness of the algorithm could induce failure. Dentler et al [243] utilized a waypoint follower based on ACO combined with a chaotic solution of a dynamical to enhance the coverage efficiency.…”

“…CPP has become a hot research topic in robotic applications such as autonomous cleaning [1,2], lawn mowing [3], structural inspection [4,5], agriculture [6,7], and surveillance [8], including exploration, mapping, search, and rescue [9,10]. Robotic end-effector could also be beneficial from CPP such as surface treatment applications (milling [11], laser cleaning [12], spray painting [13,14], fused deposition modeling printing, and manufacturing inspection [15,16]). CPP is the determination of the path that cover all points from an initial state to a final state while detecting and avoiding obstacles in a target environment [17].…”

Optimal guidance track generation for precision agriculture: A review of coverage path planning techniques

“…The Complete Coverage Path Planning (CCPP) problem arises when dealing with mobile agents engaged in operations that require an efficient route traversing every single point in a given area of interest. Classified as a subfield of industrial motion planning, solving the CCPP problem in an optimal way is highly relevant for a large number of domains, ranging from mobile robot applications such as agriculture, autonomous cleaning (Ntawumenyikizaba et al, 2012), autonomous lawn mowing (Höffmann, Clemens, et al, 2022), structural inspection (Galceran et al, 2015), and surveillance (Basilico & Carpin, 2015), to industrial or treatment applications such as milling (Kalburgi et al, 2020), laser cleaning (Ye et al, 2020) or spray painting (Kiemel et al, 2019). The use of CCPP techniques is especially essential in precision agriculture using unmanned ground vehicles or the so‐called auto‐guidance devices, as they facilitate efficient use of resources such as fuel, fertilizers, and land, reduce soil compaction, and ultimately increase yields.…”

Multi-Objective Optimization via GA Based on Micro Laser Line Scanning Data for Micro-Scale Surface Modeling

“…Additionally, ant colony has been implemented to perform free-form surface modeling in industry 4.0 [17,18], where the parameter optimization has been carried out by selecting pathways generated by the ant pheromone to accomplish the computational model. In the same way, simulated annealing has been implemented to construct free-form surface models in industry 4.0 [19,20], where the model parameters have been computed through a perturbation in an equation system. Additionally, fuzzy logic has been implemented to generate free-form surface models in industry 4.0 [21,22], where the model parameters have been optimized by using contactless scanning of the transtibial prosthetic socket.…”