Abstract: To address the problems of lengthy planned paths and low printing efficiency in 3D printing,a 3D robot motion path planning method based on an improved random tree algorithm is proposed. Based on kinematics theory,a mathematical model of the robot was established,and an improved random tree algorithm and a target bias strategy were introduced to plan the printing path. Meanwhile,to avoid task conflicts between robots,a genetic algorithm was integrated to intelligently allocate printing tasks,thereby enhancing the path planning effect. The results show that after applying the proposed method,the planned path length and the number of sampling points of the 3D printing robot are 15.64 meters and 331,respectively. In single-task planning,the planning time of the proposed method is only 1.85 seconds,with a calculation success rate of 99.47%. This method not only improves path planning efficiency but also achieves the shortest path length and effective obstacle avoidance. It helps industrial manufacturing enterprises achieve faster and higher-quality part manufacturing,providing technical support for promoting the development of the industrial manufacturing field.

Key words: random tree algorithm, 3D printing, robot, path planning, obstacle avoidance