A Study on Path Planning for Curved Surface UV Printing Robots Based on Reinforcement Learning

In robotic surface UV printing, the irregular shape of the workpiece and frequent curvature changes require the printing robot to maintain the nozzle’s perpendicular orientation to the surface during path planning, which imposes high demands on trajectory accuracy and path smoothness. To address this challenge, this paper proposes a reinforcement-learning-based path planning method. First, an ideal main path is defined based on the nozzle characteristics, and then a robot motion accuracy model is established and transformed into a Markov Decision Process (MDP) to improve path accuracy and smoothness. Next, a framework combining Generative Adversarial Imitation Learning (GAIL) and Soft Actor–Critic (SAC) methods is proposed to solve the MDP problem and accelerate the convergence of SAC training. Experimental results show that the proposed method outperforms traditional path planning methods, as well as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Specifically, the maximum Cartesian space error in path accuracy is reduced from 1.89 mm with PSO and 2.29 mm with GA to 0.63 mm. In terms of joint space smoothness, the reinforcement learning method achieves the smallest standard deviation, especially with a standard deviation of 0.00795 for joint 2, significantly lower than 0.58 with PSO and 0.729 with GA. Moreover, the proposed method also demonstrates superior training speed compared to the baseline SAC algorithm. The experimental results validate the application potential of this method in intelligent manufacturing, particularly in industries such as automotive manufacturing, aerospace, and medical devices, with significant practical value.