Time optimal trajectory planning of manipulator based on annealing-particle swarm optimization algorithm

Weihua Bai; Xingzhi Xu; Yicheng Wu

doi:10.1117/12.2661174

16 December 2022 Time optimal trajectory planning of manipulator based on annealing-particle swarm optimization algorithm

Weihua Bai, Xingzhi Xu, Yicheng Wu

Author Affiliations +

Proceedings Volume 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022); 1250066 (2022) https://doi.org/10.1117/12.2661174
Event: 5th International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 2022, Chongqing, China

Abstract

Aiming at completing the time optimal trajectory planning of 6-DOF manipulator, a time optimal 3-5-3 piecewise polynomial interpolation trajectory planning algorithm based on annealing-particle swarm optimization algorithm (SAPSO) is proposed. The 3-5-3 piecewise polynomial interpolation method divides the trajectory of the manipulator into three segments. The first, second and third sections adopt cubic polynomial interpolation, quintic polynomial interpolation and cubic polynomial interpolation respectively. Firstly, four interpolation points are set, and the 6-DOF manipulator is interpolated by 3-5-3-degree polynomial. Then, the angle, angular velocity and angular acceleration of each joint of the manipulator are taken as constraint parameters, and the shortest running time of the manipulator is taken as project to be optimized, and SAPSO is used for optimization. Finally, the MATLAB simulation results show that SAPSO has higher accuracy and speed in the process of finding the shortest running time of the manipulator than PSO. At the same time, each joint is relatively stable in the running process.

Citation Download Citation

Weihua Bai, Xingzhi Xu, and Yicheng Wu "Time optimal trajectory planning of manipulator based on annealing-particle swarm optimization algorithm", Proc. SPIE 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 1250066 (16 December 2022); https://doi.org/10.1117/12.2661174

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