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DU Yuxiao, CHEN Yihang. Time Optimal Trajectory Planning Algorithm for Robotic Manipulator Based on Locally Chaotic Particle Swarm Optimization[J]. Chinese Journal of Electronics. doi: 10.1049/cje.2021.00.373
Citation: DU Yuxiao, CHEN Yihang. Time Optimal Trajectory Planning Algorithm for Robotic Manipulator Based on Locally Chaotic Particle Swarm Optimization[J]. Chinese Journal of Electronics. doi: 10.1049/cje.2021.00.373

Time Optimal Trajectory Planning Algorithm for Robotic Manipulator Based on Locally Chaotic Particle Swarm Optimization

doi: 10.1049/cje.2021.00.373
Funds:  This work was supported by the National Natural Science Foundation of China (61976059)
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  • Author Bio:

    was born in 1973. He received the Ph.D. degree in School of Information Science and Engineering, Central South University, Changsha, China, in 2004. He is an Associate Professor of School of Automation, Guangdong University of Technology. His research interests include intelligent manufacturing and industrial robot technology and brain computer interface technology. (Email: yuxiaodu@gdut.edu.cn)

    (corresponding author) was born in 1997. He received the B.E. degree in mechanical engineering, Guangdong University of Technology, Guangzhou, China, in 2019. He is currently pursing the M.E. degree in control science and engineering from Guangdong University of Technology, Guangzhou, China. His research interests include industrial robot and intelligent manufacturing. (Email: gdut_cyh@163.com)

  • Received Date: 2021-10-21
  • Accepted Date: 2022-02-11
  • Available Online: 2022-03-12
  • Optimal trajectory planning is a fundamental problem in the area of robotic research. On the time-optimal trajectory planning problem during the motion of a robotic arm, the method based on segmented polynomial interpolation function with a locally chaotic particle swarm optimization (LCPSO) algorithm is proposed in this paper. While completing the convergence in the early or middle part of the search, the algorithm steps forward on the problem of local convergence of traditional particle swarm optimization (PSO) and improved learning factor PSO (IFPSO) algorithms. Finally, simulation experiments are executed in joint space to obtain the optimal time and smooth motion trajectory of each joint, which shows that the method can effectively shorten the running time of the robotic manipulator and ensure the stability of the motion as well.
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