Skip to main content
SpringerLink
Log in

Trajectory optimization of an electro-hydraulic robot

  • Original Article
  • Published:
Journal of Mechanical Science and Technology Aims and scope Submit manuscript

Abstract

The electro-hydraulic robot has great driving torque and plays an irreplaceable role in manufacturing. In this paper, a trajectory planning method for a 6-DOF electro-hydraulic robot based on time-energy-jerk is proposed and realized. A multi-objective function of the electro-hydraulic robot about working efficiency, impact, stability and energy consumption factors was built, at the same time, the kinematic and dynamic constraint functions were also built. The multi-objective trajectory function of the robot was optimized by using the non-dominated neighborhood immune genetic algorithm, and the optimal position, velocity, acceleration and jerk planning curves of each joint were obtained. In the experiment, the flow output curves of the hydraulic system of joint 6 based on fuzzy-PID control strategy under no-load, 1.5 kg load, 4 kg load were obtained, and show that the velocity and position trajectory of the joint can be well controlled using the presented fuzzy PID control strategy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. A. Gasparetto, P. Boscariol, A. Lanzutti and R. Vidoni, Path planning and trajectory planning algorithms: a general overview, Motion and Operation Planning of Robotic Systems, 29 (2015) 3–27.

    Article  Google Scholar 

  2. M. Shimizu, K. Kobayashi and K. Watanabe, Clothoidal curve-based path generation for an autonomous mobile robot, SICE-ICCAS 2006 (2006) 478–481.

  3. A. Gasparetto and V. Zanotto, A technique for time-jerk optimal planning of robot trajectories, Robotics and Computer Integrated Manufacturing, 24(3) (2007) 415–426.

    Article  Google Scholar 

  4. Y. Y. Liang, M. S. Lin and Y. L. Lai, The research on robot trajectory planning algorithm based on cubic non-uniform B-spline curve, Science Technology & Engineering, 13(35) (2013) 10511–10517.

    Google Scholar 

  5. Z. J. Gou and W. Cheng, The trajectory planning and simulation for industrial robot based on fifth-order B-splines, Mechanical, Electronic and Engineering Technologies (ICMEET 2014), 538 (2014) 367.

    Google Scholar 

  6. R. Saravanan, S. Ramabalan and C. Balamurugan, Multiobjective trajectory planner for industrial robots with payload constraints, Robotica, 26(6) (2008) 753–765.

    Article  Google Scholar 

  7. C. D. Wang, X. J. Wang and H. Zheng, Trajectory planning for a welding robot based on the bezier curve, International J. of Control & Automation, 8(2) (2015) 351–362.

    Article  Google Scholar 

  8. Y. Yokose, Energy-saving trajectory planning for robots using the genetic algorithm with assistant chromosomes, Artificial Life and Robotics, 25(1) (2020) 89–93.

    Article  Google Scholar 

  9. Y. Shubin et al., A machine learning based energy efficient trajectory planning approach for industrial robots, Procedia CIRP, 81 (2019) 429–434.

    Article  Google Scholar 

  10. L. Sichao et al., Energy-efficient trajectory planning for an industrial robot using a multi-objective optimisation approach, Procedia Manufacturing, 25 (2018) 517–525.

    Article  Google Scholar 

  11. C. T. Chen and T. T. Liao, A hybrid strategy for the time- and energy-efficient trajectory planning of parallel platform manipulators, Robotics and Computer-Integrated Manufacturing, 27(1) (2011) 72–81.

    Article  Google Scholar 

  12. C. Lin et al., Formulation and optimization of cubic polynomial joint trajectories for industrial robots, Automatic Control IEEE Transactions on Automatic Control, 28(12) (1983) 1066–1074.

    Article  Google Scholar 

  13. J. E. Bobrow et al., Optimal robot motions for physical criteria, J. of Robotic Systems, 18(12) (2001) 785–795.

    Article  Google Scholar 

  14. D. H. Qian, F. Tan and X. F. Zhao, Time optimal trajectory planning of robot based on B-spline path, J. of Shanghai Jiaotong University (1998) 31–35.

  15. J. T. Yang, W. G. Jiang and Y. C. Lin, Trajectory planning algorithm for optimal impact of industrial robots, Science Technology and Engineering (2014) 64–69.

  16. Y. Yue et al., NURBS trajectory planning of 6-DOF industrial robot based on optimization, Modular Machine Tool & Automatic Manufacturing Technique (2017) 41–43.

  17. J. Gregory, A. Olivares and E. Staffetti, Energy-optimal trajectory planning for robot manipulators with holonomic constraints, Systems & Control Letters, 61(2) (2012) 279–291.

    Article  MathSciNet  Google Scholar 

  18. Z. P. Luo, Research on motion trajectory planning method of Industrial robot based on optimal energy, Master Thesis, Hunan University (2015).

  19. C. Wang, Energy-optimal trajectory planning of 6-DOF serial robot, Master Thesis, Changchun University of Technology (2014).

  20. S. G. Liu, Research on motion optimization and trajectory tracking control of 6-DOF series robot, Doctorial Thesis, Zhejiang University (2009).

  21. H. F. Wang, Multi-objective trajectory optimization and motion control of tandem robots, Doctorial Thesis, Zhejiang University (2011).

  22. Y. Shen, Research on trajectory planning and control algorithms of 6-DOF industrial robot, Master Thesis, Nanjing University of Science and Technology (2017).

  23. A. Gasparetto and V. Zanotto, A new method for smooth trajectory planning of robot manipulators, Mechanism & Machine Theory, 42(4) (2007) 455–471.

    Article  MathSciNet  Google Scholar 

  24. H. F. Wang, S. Q. Zhu and W. X. Wu, Multi-objective trajectory planning of manipulator based on INSGA-11 algorithm, J. of Zhejiang University (2012) 622–628.

  25. T. He, Y. Zhang, F. Sun and X. Shi, Immune optimization based multi-objective six-DOF trajectory planning for industrial robot manipulators, Intelligent Control & Automation (2016) 2945–2950.

  26. M. H. Jin, P. H. Li and J. J. Xie, Research on multi-objective comprehensive trajectory planning of space manipulator, Machinery & Electronics (2018) 34–38.

  27. J. S. Huang, P. F. Hu, K. Y. Wu and M. Zeng, Optimal time-jerk trajectory planning for industrial robots, Mechanism and Machine Theory, 121 (2018) 530–544.

    Article  Google Scholar 

  28. L. Guo, H. Fang and X. Shi, Multi-objective optimal trajectory planning of manipulators based on quintic NURBS, IEEE International Conference on Mechatronics and Automation (2016) 759–765.

  29. R. K. Barai and K. Nonami, Locomotion control of a hydraulically actuated hexapod robot by robust adaptive fuzzy control with self-tuned adaptation gain and dead zone fuzzy pre-compensation, J. of Intelligent and Robotic Systems, 53 (2008) 35–56.

    Article  Google Scholar 

  30. M. Y. Hassan and G. Kothapalli, Interval type-2 fuzzy position control of electro-hydraulic actuated robotic excavator, International J. of Mining Science and Technology, 22 (2012) 437–445.

    Article  Google Scholar 

  31. C. Yang, Q. Huang and J. Han, Decoupling control for spatial six-degree-of-freedom electro-hydraulic parallel robot, Robotics and Computer-Integrated Manufacturing, 28(1) (2012) 14–23.

    Article  Google Scholar 

  32. D. Y. Zhang, Research on trajectory tracking electro-hydraulic servo control system of robot excavator, Master Thesis, Zheji-ang University (2014).

  33. X. Y. Meng, W. J. Ge and X. L. Tan, Trajectory tracking control of compliant hopping robot including actuator dynamics, Mechanical Science and Technology for Aerospace Engineering, 36 (2017) 165–171.

    Google Scholar 

  34. L. Y. Tian, F. Zhou, P. H. Zhang and Z. H. Chen, Motion control method of underwater vehicle with hydraulic thrusters, Mechanical & Electrical Engineering Magazine, 35 (2018) 694–697.

    Google Scholar 

  35. J. Koivumäki, W. Zhu and J. Mattila, Energy-efficient and high-precision control of hydraulic robots, Control Engineering Practice, 85 (2019) 176–193.

    Article  Google Scholar 

  36. L. S. Wu, X. Q. Gao, H. Xiong and H. W. Chen, Improved curve surface seamless splicing based on NURBS, Optics and Precision Engineering (2013) 431–436.

Download references

Acknowledgments

This work was supported in part by Sichuan Science and Technology Plan Project under Grant No. 2019JDRC0087.

Author information

Authors and Affiliations

  1. College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, 610059, China

    Xueju Peng, Yingjie Tang, Changwei Miao & Yang Li

  2. College of Information Science and Technology, Chengdu University of Technology, Chengdu, 610059, China

    Guangzhu Chen

Authors
  1. Xueju Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guangzhu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yingjie Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Changwei Miao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guangzhu Chen.

Additional information

Xueju Peng received his Master of Engineering from Chengdu University of Technology in 2019. His research interests include mechanical-electrical integration, robot control.

Guangzhu Chen is a Professor of the College of Information Science and Technology, Chengdu University of Technology. He received his Ph.D. in Computer Application from Sichuan University. His research interests include robot, mechatronics, artificial intelligence, and machine vision.

Yingjie Tang received his Master of Engineering from Chengdu University of Technology in 2020. His research interests include robot vision control and deep learning.

Changwei Miao received his Master of Engineering from Chengdu University of Technology in 2020. His research interests include mechatronics and robot trajectory planning.

Yang Li is currently a graduate student in the College of Nuclear Technology and Automation Engineering, Chengdu University of Technology. His research interests include mobile robot navigation and machine vision.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Peng, X., Chen, G., Tang, Y. et al. Trajectory optimization of an electro-hydraulic robot. J Mech Sci Technol 34, 4281–4294 (2020). https://doi.org/10.1007/s12206-020-0919-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12206-020-0919-4

Keywords

Search

Navigation