Exploring Time-optimal Trajectory of Automatic Charging Manipulator with Improved Particle Swarm Optimization Algorithm

ZHU Hao; ZHAO Qinghai; ZHENG Qunfeng; NING Changjiu

doi:10.13433/j.cnki.1003-8728.20220271

Volume 43 Issue 3

Mar. 2024

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Article Navigation > Mechanical Science and Technology for Aerospace Engineering > 2024 > 43(3): 423-429

ZHU Hao, ZHAO Qinghai, ZHENG Qunfeng, NING Changjiu. Exploring Time-optimal Trajectory of Automatic Charging Manipulator with Improved Particle Swarm Optimization Algorithm[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(3): 423-429. doi: 10.13433/j.cnki.1003-8728.20220271

Citation:

ZHU Hao, ZHAO Qinghai, ZHENG Qunfeng, NING Changjiu. Exploring Time-optimal Trajectory of Automatic Charging Manipulator with Improved Particle Swarm Optimization Algorithm[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(3): 423-429. doi: 10.13433/j.cnki.1003-8728.20220271

Citation:

ZHU Hao, ZHAO Qinghai, ZHENG Qunfeng, NING Changjiu. Exploring Time-optimal Trajectory of Automatic Charging Manipulator with Improved Particle Swarm Optimization Algorithm[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(3): 423-429. doi: 10.13433/j.cnki.1003-8728.20220271

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Exploring Time-optimal Trajectory of Automatic Charging Manipulator with Improved Particle Swarm Optimization Algorithm

doi: 10.13433/j.cnki.1003-8728.20220271

1.
School of Mechanical and Electrical Engineering, Qingdao University, Qingdao 266071, Shandong, China
2.
National Engineering Research Center for Intelligent Electrical Vehicle Power System, Qingdao University, Qingdao 266071, Shandong, China
3.
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

Received Date: 2022-03-05
Publish Date: 2024-03-25

Abstract

Abstract

A particle swarm optimization (PSO) algorithm based on the nonlinear dynamic learning factor was proposed to solve the time optimization problem in the joint space trajectory planning of a truss charging manipulator. The workspace was obtained through kinematic analysis, and the 3-5-3 polynomial interpolation was introduced for the trajectory planning. The shortest motion time was sought through combining velocity constraints with acceleration constraints. The convergence speed of the improved PSO algorithm was compared with that of the basic PSO algorithm, and the variation of motion time of each joint before and after optimization was analyzed. The simulation results show that the convergence performance of the improved PSO algorithm is faster than that of the basic PSO algorithm and that the overall motion time is shortened by about 33%, confirming the feasibility of the improved PSO algorithm.
- truss charging manipulator,
- time optimization,
- nonlinear dynamic learning factor,
- particle swarm optimization algorithm

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References(15)

References

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