Abstract: Coverage path planning (CPP) is an essential process in robotic grinding, particularly with the increasing demand for large-scale multiconnected free-form surfaces, such as high-speed rail shells, car shells, and aeronautical parts. Owing to its multi-connectivity, achieving full coverage with a single continuous path is challenging. Additionally, large curvatures make the path spacing difficult to control, leaving some areas uncovered. Existing methods often fail to optimize continuity and coverage rates simultaneously, resulting in redundant tool-feeding and lifting processes that significantly reduce processing efficiency. Thus, a novel method for free-form surface CPP is proposed based on reinforcement learning (RL), which enables the learning of an optimal path with optimized continuity and coverage rates. Specifically, to regulate the path spacing, a uniform grid map is constructed based on the least-squares conformal mapping (LSCM) method, which parameterizes the grinding surface to a two-dimensional (2D) plane with controllable distortion. Furthermore, a CPP-specific evaluation criteria (CEC) is designed to evaluate the path through various key factors, including coverage rate, continuity, and smoothness. Finally, a grinding path is generated using the CEC-guided RL framework. The method was verified through several simulations, and a grinding experiment on a high-speed rail head surface was conducted as a typical application. The results showed high path continuity and coverage rates, demonstrating its potential for addressing CPP problems in different manufacturing scenarios.

The full text can be downloaded at https://doi.org/10.1007/s40436-025-00570-z

Key words: Coverage path planning (CPP), Grinding, Reinforcement learning (RL), Muti-connected free-form surface, Reward function construction