Zhengyang He, Xiaojie Tang, Qin Shen, Chenxu Duan, and Chengfen Jia
Trajectory, joint space, non-uniform rational B-splines, improved multi-objective, particle swarm optimisation
In order to improve the operation efficiency and mechanical performance of six-degree-of-freedom manipulators, an improved multi-objective particle swarm optimisation (IMPSO) algorithm is proposed to optimise the trajectory of the KUKA KR16 joint space fitted with the quintic non-uniform rational B-splines (NURBS) curve. The improved non-dominated sorting genetic algorithm-II (NSGA-II) is adopted to sort the particles. The mutation strategy, dynamic weighting method, and normalised weight multi-objective function are established in the IMPSO. The improved multi- objectives, the biggest innovation, are adopted particularly in the evaluations of individual optimal particles and global optimal particles. The best six individual particles are evaluated in terms of the individual total time and the mean angular acceleration and jerk. The global optimal particle is evaluated in terms of the total time, the total mean joint torque and jerk of the six joints. The optimal Pareto is obtained within the kinematic constraints of the robot. The proposed method is experimental compared with a current KUKA KR16 experiment, where the manipulator end passes through a selected set of path points. In comparison to the current robot, the results using the IMPSO algorithm show that the running time of each individual joint is shorter and the trajectory in joint space is smoother.
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