Turn off MathJax
Article Contents
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)
More Information
  • 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.
  • loading
  • [1]
    Abu-Dakka F J, Rubio F, Valero F, et al., “Evolutionary indirect approach to solving trajectory planning problem for industrial robots operating in workspaces with obstacles,” European Journal of Mechanics A/Solids, vol.42, pp.210–218, 2013. doi: 10.1016/j.euromechsol.2013.05.007
    L. Cui, H. Wang, and W. Chen, “Trajectory planning of a spatial flexible manipulator for vibration suppression,” Robotics and Autonomous Systems, Vol.123, article no.103316, 2019.
    L. Jin, S. Li, J. Yu, et al., “Robot manipulator control using neural networks: A survey,” Neurocomputing, vol.285, pp.23–34, 2018. doi: 10.1016/j.neucom.2018.01.002
    L. Tang, X. Tang, X. Jiang, et al., “Dynamic trajectory planning study of planar two-dof redundantly actuated cable-suspended parallel robots,” Mechatronics, vol.30, pp.187–197, 2015. doi: 10.1016/j.mechatronics.2015.07.005
    Y. Mu, L. Zhang, X. Chen, et al., “Optimal trajectory planning for robotic manipulators using chicken swarm optimization,” in Proc. of 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China, pp.369–373, 2016.
    Y. Yang, Z. Li, X. Yu, et al., “A trajectory planning method for robot scanning system using mask R-CNN for scanning objects with unknown model,” Neurocomputing, vol.404, pp.329–339, 2020. doi: 10.1016/j.neucom.2020.04.059
    N. Zeng, H. Zhang, Y. Chen, et al., “Path planning for intelligent robot based on switching local evolutionary PSO algorithm,” Assembly Automation, vol.36, no.2, pp.120–126, 2016. doi: 10.1108/AA-10-2015-079
    J. Wei, M. Jin, and Y. Liu, “A new method to reduce energy in trajectory planning,” in Proc. of 2020 6th International Conference on Control, Automation and Robotics, Singapore, pp.1–6, 2016.
    O. Avram and A. Valente, “Trajectory planning for reconfigurable industrial robots designed to operate in a high precision manufacturing industry,” Procedia CIRP, vol.57, pp.461–466, 2016. doi: 10.1016/j.procir.2016.11.080
    W. Liu, Z. Wang, Y. Yuan, et al., “A novel sigmoid-function-based adaptive weighted particle swarm optimizer,” IEEE Transactions on Cybernetics, vol.51, no.2, pp.1085–1093, 2021. doi: 10.1109/TCYB.2019.2925015
    S. Piao, Q. ZHONG, and Y. Liu, “The research of optimal motion planning for robot in complex environment,” Chinese Journal of Electronics, vol.20, no.4, pp.637–640, 2011.
    G. Antonelli, S. Chiaverini, G. P. Gerio, M. Palladino, et al., “SmartMove4: An industrial implementation of trajectory planning for robots,” Industrial Robot, vol.34, no.3, pp.217–224, 2007. doi: 10.1108/01439910710738854
    A. Reiter, A. Müller, and H. Gattringer, “Inverse kinematics in minimum-time trajectory planning for kinematically redundant manipulators,” in Proc. of IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy, pp.6873–6878, 2016.
    J. Kim, S. Kim, S. Kim, et al., “A practical approach for minimum-time trajectory planning for industrial robots,” Industrial Robot, vol.37, no.1, pp.51–61, 2010. doi: 10.1108/01439911011009957
    A. Aloulou and O. Boubaker, Motion and Operation Planning of Robotic Systems, Springer, Cham, pp.385–415, 2015.
    A. Valente, S. Baraldo, and E. Carpanzano, “Smooth trajectory generation for industrial robots performing high precision assembly processes,” CIRP Annals, vol.66, pp.17–20, 2017. doi: 10.1016/j.cirp.2017.04.105
    R. Menasri, H. Oulhadj, B. Daachi, et al., “Smooth trajectory planning for robot using particle swarm optimization,” in Proc. of International Conference on Swarm Intelligence Based Optimization, Mulhouse, France, pp.50–59, 2014.
    W. Zhang and S. Fu, “Time-optimal trajectory planning of dulcimer music robot based on PSO algorithm,” in Proc. of 2020 Chinese Control And Decision Conference, Hefei, China, pp.4769–4774, 2020.
    M. Gao, P. Ding, and Y. Yang, “Time-optimal rrajectory planning of industrial robots based on particle swarm optimization,” in Proc. of 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control, Qinhuangdao, China, pp.1934–1939, 2015.
    C. Liu, G. Cao, Y. Qu, et al., “An improved PSO algorithm for time-optimal trajectory planning of Delta robot in intelligent packaging,” The International Journal of Advanced Manufacturing Technology, vol.107, no.3, pp.1091–1099, 2020.
    K. Petrinec and Z. Kovacic, “Trajectory planning algorithm based on the continuity of jerk”, in Proc. of 2007 Mediterranean Conference on Control & Automation, Athens, Greece, pp.1–5, 2007.
    I. U. Rahman, Z. Wang, W. Liu, et al., “An N-state markovian jumping particle swarm optimization algorithm,” IEEE Trans. on Systems, Man, and Cybernetics: Systems, vol.51, no.11, pp.6626–2238, 2021. doi: 10.1109/TSMC.2019.2958550
    T. Yan, F. Liu, and B. Chen, “New particle swarm optimisation algorithm with Hénon chaotic map structure,” Chinese Journal of Electronics, vol.26, no.4, pp.747–753, 2017. doi: 10.1049/cje.2017.06.006
    N. Zeng, D. Song, H. Li, et al., “A competitive mechanism integrated multi-objective whale optimization algorithm with differential evolution,” Neurocomputing, vol.432, pp.170–182, 2020.
    N. Zeng, H. Qiu, Z. Wang, et al., “A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer’s disease,” Neurocomputing, vol.320, pp.195–202, 2018. doi: 10.1016/j.neucom.2018.09.001
    X. Huang, P. Jia, B. Liu, et al., “Control of Hénon chaotic systems by chaotic particle swarm optimization,” in Proc. of 9th IEEE International Conference on Cognitive Informatics, Beijing, China, pp.117–121, 2010.
    W. Hong, “Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model,” Energy Conversion & Management, vol.50, pp.105–117, 2009.
    M. Li, W. Hong, and H. Kang, “Urban traffic flow forecasting using Gauss-SVR with cat mapping, cloud model and PSO hybrid algorithm,” Neurocomputing, vol.99, pp.230–240, 2013. doi: 10.1016/j.neucom.2012.08.002
    X. Zou, W. Liu, and G. Feng, “Applying chaotic maps to interleaving scheme design in BICM-ID,” Chinese Journal of Electronics, vol.19, no.3, pp.521–524, 2010.
    G. Kaddoum, “Wireless Chaos-Based Communication Systems: A comprehensive survey,” IEEE Access, vol.4, pp.2621–2648, 2016. doi: 10.1109/ACCESS.2016.2572730
    X. Yi, “Hash function based on chaotic tent maps,” IEEE Transactions on Circuits and Systems Ⅱ: Express Briefs, vol.52, no.6, pp.354–357, 2005. doi: 10.1109/TCSII.2005.848992
    J. Zhao, H. Wang, W. Liu, et al., “A learning-based multiscale modelling approach to real-time serial manipulator kinematics simulation,” Neurocomputing, vol.390, pp.280–293, 2020. doi: 10.1016/j.neucom.2019.04.101
    H.N. Nguyen, J. Zhou, and H.J. Kang, “A calibration method for enhancing robot accuracy through integration of an extended Kalman filter algorithm and an artificial neural network,” Neurocomputing, vol.151, pp.996–1005, 2015. doi: 10.1016/j.neucom.2014.03.085
    Ü. Dinȩr and M. ȩvik, “Improved trajectory planning of an industrial parallel mechanism by a composite polynomial consisting of Bézier curves and cubic polynomials,” Mechanism and Machine Theory, vol.132, pp.248–263, 2019. doi: 10.1016/j.mechmachtheory.2018.11.009
  • 加载中


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(9)  / Tables(5)

    Article Metrics

    Article views (124) PDF downloads(15) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint