Route Planning through Genetic Algorithm for Multi-Axis Motion Control

Route planning is crucial in the manufacturing industry, especially in cutting systems. Multi-axis motion control is pivotal to the laser cutting process in that it substantially enhances the efficiency by moving the source device along two axes simultaneously to target the workpiece. However, optimizing the routes for multi-axis motion control remains a complex challenge due to the need for coordination and efficiency. This study formulates it as a close-enough traveling salesman problem (CETSP) and designs a genetic algorithm (GA) for solving this routing problem. First, the line simplification algorithm is employed to convert the cutting pattern into a series of points, serving as the city points in the CETSP. Second, a structural approach is presented to address the difficulty in determining the turning points. For the GA, we developed crossover and mutation operators to improve its effectiveness and efficiency. The experimental results show that the proposed GA reduces route lengths by 17–28% and improves the variable speed in the route by 47–69% on two test cutting patterns.