Multi-objective trajectory planning of redundant manipulator based on MOSPO

Conference: CIBDA 2022 - 3rd International Conference on Computer Information and Big Data Applications
03/25/2022 - 03/27/2022 at Wuhan, China

Proceedings: CIBDA 2022

Pages: 5Language: englishTyp: PDF

Authors:
Wang, Wenli; Wang, Peng; Zhao, Yongguo (Qilu University of Technology (Shandong Academy of Sciences), Institute of automation, Shandong Academy of Sciences, Shandong Provincial Key Laboratory of Robot and Manufacturing Automation Technology, Shandong, China)
Zhu, Yunhai (Qilu University of Technology (Shandong Academy of Sciences), High-tech Industry (Pilot) Base of Shandong Academy of Sciences, Shandong, China)

Abstract:
A multi-objective trajectory planning study is conducted for the 7-degree-of-freedom robotic arm of AUBO's MRP1 experimental platform with the objectives of optimal time, optimal energy consumption and optimal pulsation work index. Firstly, the forward kinematic equations of this robotic arm are constructed according to the improved D-H method, the correctness of the forward and inverse kinematic solution process is verified, the point cloud map of the workspace is generated using the Monte Carlo method, and the boundary points of the workspace are extracted to analyse the workspace of this robotic arm. Secondly, for the multi-objective optimisation problem of running time, energy consumption and trajectory pulsation, the mathematical model of the working trajectory is established by using the fivepolynomial interpolation method, and the multi-objective particle swarm algorithm (MOPSO) is proposed to optimise its trajectory under the kinematic constraints of the robotic arm to obtain the Pareto optimal solution set and select the desired solution. Simulation results show that the proposed five-polynomial interpolation method can accurately construct a smooth trajectory while completing the multi-objective optimisation under kinematic constraints by the particle swarm algorithm to obtain the desired Pareto distribution. Finally, the objective function is constructed to obtain an optimised trajectory that meets the requirements and solves the problems of excessive running time, excessive energy consumption and pulsation shocks, thus ensuring efficient robot motion.