Resumen
To address the issues of low efficiency and lengthy running time associated with trajectory planning for 6-degree-of-freedom manipulators, this paper introduces a novel solution that generates a time-optimal path for a manipulator while adhering to its kinematic limitations. The proposed method comprises several stages. Firstly, the kinematics of the manipulator are analyzed. Secondly, the manipulator?s joint-space path points are interpolated via the quintic B-spline curve. Subsequently, the non-dominated sorting genetic algorithm II (NSGA-II) is improved by applying reinforcement learning to optimize its crossover and mutation probabilities, and the variable neighborhood search (VNS) algorithm is integrated to enhance its local search capability. Finally, the joint increments and running time of the manipulator are optimized using the improved NSGA-II, and the time-optimal trajectory is then determined by simulation on MATLAB. Furthermore, compared with other conventional optimization methods, the proposed approach has reduced the total running time by 19.26%, effectively improving the working efficiency of the manipulator.