Time-Optimal Trajectory Planning for the Manipulator Based on Improved Non-Dominated Sorting Genetic Algorithm II

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 time-optimal path manipulator while adhering to its kinematic limitations. The proposed method comprises several stages. Firstly, kinematics are analyzed. Secondly, manipulator’s joint-space points interpolated via quintic B-spline curve. Subsequently, non-dominated sorting genetic algorithm II (NSGA-II) is improved by applying reinforcement learning optimize crossover mutation probabilities, variable neighborhood search (VNS) integrated enhance local capability. Finally, joint increments optimized using NSGA-II, then determined simulation on MATLAB. Furthermore, compared other conventional optimization methods, approach has reduced total 19.26%, effectively improving working manipulator.