Shao-Lin Zhang, Yue-Guang Ge, Hai-Tao Wang, Shuo Wang. Mechanical Design and Dynamic Compliance Control of Lightweight Manipulator. International Journal of Automation and Computing, vol. 18, no. 6, pp.926-934, 2021. https://doi.org/10.1007/s11633-021-1311-2
Citation: Shao-Lin Zhang, Yue-Guang Ge, Hai-Tao Wang, Shuo Wang. Mechanical Design and Dynamic Compliance Control of Lightweight Manipulator. International Journal of Automation and Computing, vol. 18, no. 6, pp.926-934, 2021. https://doi.org/10.1007/s11633-021-1311-2

Mechanical Design and Dynamic Compliance Control of Lightweight Manipulator

doi: 10.1007/s11633-021-1311-2
More Information
  • Author Bio:

    Shao-Lin Zhang received the B. Eng. and M. Eng. degrees in mechanical science and engineering from Huazhong University of Science and Technology, China in 2010 and 2013, and the Ph. D. degree in control theory and control engineering from Institute of Automation, Chinese Academy of Sciences, China in 2019. He is currently an assistant professor with State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China. His research interests include intelligent robot system and automation systems. E-mail: zhangshaolin2015@ia.ac.cn ORCID iD: 0000-0002-1544-2552

    Yue-Guang Ge received the B. Eng. degree in computer science and technology from Northeastern University, China in 2006, the M. Eng. degree in computer software and theory from North China Electric Power University, China in 2014. He is a Ph. D. degree candidate in control theory and control engineering at University of Chinese Academy of Sciences, China. His research interests include intelligent robot system, knowledge representation and reasoning. E-mail: yueguang.ge@ia.ac.cn ORCID iD: 0000-0001-6850-2179

    Hai-Tao Wang received the B. Eng. degree in communication engineering from University of Electronic Science and technology of China, China in 2014. He is a Ph. D. degree candidate in control theory and control engineering at University of Chinese Academy of Sciences, Institute of Automation, Chinese Academy of Sciences, China. His research interests include robot task planning and knowledge reasoning. E-mail: wanghaitao2019@ia.ac.cn ORCID iD: 0000-0002-5297-9543

    Shuo Wang received the B. Eng. degree in electrical engineering from the Shenyang Architecture and Civil Engineering Institute, China in 1995, the M. Eng. degree in industrial automation from Northeastern University, China in 1998, and the Ph. D. degree in control theory and control engineering from Institute of Automation, Chinese Academy of Sciences, China in 2001, respectively. He is currently a professor with State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China. His research interests include biomimetic robot, underwater robot, and multirobot systems. E-mail: shuo.wang@ia.ac.cn (Corresponding author) ORCID iD: 0000-0002-1390-9219

  • Received Date: 2021-06-09
  • Accepted Date: 2021-09-26
  • Publish Online: 2021-11-06
  • Publish Date: 2021-12-01
  • In the existing modular joint design and control methods of collaborative robots, the inertia of the manipulator link is large, the dynamic trajectory planning ability is weak, the collision stop safety strategy is dependent, and the adaptability and safety to the changing environment are limited. This paper develops a six-degree-of-freedom lightweight collaborative manipulator with real-time dynamic trajectory planning and active compliance control. Firstly, a novel motor installation, joint transmission, and link design method is put forward to reduce the inertia of the links and improve intrinsic safety. At the same time, to enhance the dynamic operation capability and quick response of the manipulator, a smooth planning of position and orientation under initial/end pose and velocity constraints is proposed. The adaptability to the environment is improved by the active compliance control. Finally, experiments are carried out to verify the effectiveness of the proposed design, planning, and control methods.

     

  • loading
  • [1]
    A. Albu-Schäffer, S. Haddadin, C. Ott, A. Stemmer, T. Wimböck, G. Hirzinger. The DLR lightweight robot: Design and control concepts for robots in human environments. Industrial Robot, vol. 34, no. 5, pp. 376–385, 2007. DOI: 10.1108/01439910710774386.
    [2]
    Q. Liu, D. G. Yang, W. D. Hao, Y. Wei. Research on kinematic modeling and analysis methods of UR robot. In Proceedings of the 4th IEEE Information Technology and Mechatronics Engineering Conference, IEEE, Chongqing, China, pp. 159−164, 2018.
    [3]
    X. L. Zhu, X. M. Dun, L. Shan, X. Y. Dun. Study and design of a light-weight multi-joint discretely-actuated manipulator. Machine Tool &Hydraulics, vol. 42, no. 21, pp. 1–5, 2014. DOI: 10.3969/j.issn.1001-3881.2014.21.001. (in Chinese)
    [4]
    H. Song, Y. S. Kim, J. Yoon, S. H. Yun, J. Seo, Y. J. Kim. Development of low-inertia high-stiffness manipulator LIMS2 for high-speed manipulation of foldable objects. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Madrid, Spain, pp. 4145−4151, 2018. DOI: 10.1109/IROS.2018.8594005.
    [5]
    N. J. Liu, T. Lu, Y. H. Cai, S. Wang. A review of robot manipulation skills learning methods. Acta Automatica Sinica, vol. 45, no. 3, pp. 458–470, 2019. DOI: 10.16383/j.aas.c180076. (in Chinese)
    [6]
    S. L. Zhang, F. S. Jing, S. Wang. A transition method based on Bezier curve for trajectory planning in Cartesian space. High Technology Letters, vol. 23, no. 2, pp. 141–148, 2017. DOI: 10.3772/j.issn.1006-6748.2017.02.004.
    [7]
    S. L. Zhang, F. S. Jing, S. Wang. Orientation transition and interpolation method based on spherical Bezier. Journal of Huazhong University of Science and Technology (Nature Science Edition), vol. 45, no. 10, pp. 75–79, 2017. DOI: 10.13245/j.hust.171014. (in Chinese)
    [8]
    F. Miyazaki, M. Matsushima, M. Takeuchi. Learning to dynamically manipulate: A table tennis robot controls a ball and rallies with a human being. Advances in Robot Control: From Everyday Physics to Human-Like Movements, S. Kawamura, M. Svinin, Eds., Berlin, Germany: Springer, pp. 317−341, 2006. DOI: 10.1007/978-3-540-37347-6_15.
    [9]
    K. Mülling, J. Peters. A computational model of human table tennis for robot application. Autonome Mobile Systeme 2009, R. Dillmann, J. Beyerer, C. Stiller, J. M. Zöllner, T. Gindele, Eds., Berlin, Germany: Springer, pp. 57-64, 2009. DOI: 10.1007/978-3-642-10284-4_8.
    [10]
    A. Gasparetto, V. Zanotto. Optimal trajectory planning for industrial robots. Advances in Engineering Software, vol. 41, no. 4, pp. 548–556, 2010. DOI: 10.1016/j.advengsoft.2009.11.001.
    [11]
    Y. Liu, L. Shi, X. C. Tian. Weld seam fitting and welding torch trajectory planning based on NURBS in intersecting curve welding. The International Journal of Advanced Manufacturing Technology, vol. 95, no. 5, pp. 2457–2471, 2018. DOI: 10.1007/s00170-017-1374-y.
    [12]
    Z. J. Li, H. B. Wu, J. M. Yang, M. H. Wang, J. H. Ye. A position and torque switching control method for robot collision safety. International Journal of Automation and Computing, vol. 15, no. 2, pp. 156–168, 2018. DOI: 10.1007/S11633-017-1104-9.
    [13]
    H. Matsumori, M. C. Deng, Y Noge. An operator-based nonlinear vibration control system using a flexible arm with shape memory alloy. International Journal of Automation and Computing, vol. 17, no. 1, pp. 139–150, 2020. DOI: 10.1007/s11633-018-1149-4.
    [14]
    J. J. Duan, Y. H. Gan, M. Chen, X. Z. Dai. Adaptive variable impedance control for dynamic contact force tracking in uncertain environment. Robotics and Autonomous Systems, vol. 102, pp. 54–65, 2018. DOI: 10.1016/j.robot.2018.01.009.
    [15]
    Z. J. Li, T. Zhao, F. Chen, Y. B. Hu, C. Y. Su, T. Fukuda. Reinforcement learning of manipulation and grasping using dynamical movement primitives for a Humanoidlike mobile manipulator. IEEE/ASME Transactions on Mechatronics, vol. 23, no. 1, pp. 121–131, 2017. DOI: 10.1109/TMECH.2017.2717461.
    [16]
    B. Sangiovanni, A. Rendiniello, G. P. Incremona, A. Ferrara, M. Piastra. Deep reinforcement learning for collision avoidance of robotic manipulators. In Proceedings of European Control Conference, IEEE, Limassol, Cyprus, pp. 2063−2068, 2018. DOI: 10.23919/ECC.2018.8550363.
    [17]
    A. H. Barr, B. Currin, S. Gabriel, J. F. Hughes. Smooth interpolation of orientations with angular velocity constraints using quaternions. ACM SIGGRAPH Computer Graphics, vol. 26, no. 2, pp. 313–320, 1992. DOI: 10.1145/142920.134086.
    [18]
    M. Jafari, H. Molaei. Spherical linear interpolation and Bézier curves. General Scientific Researches, vol. 2, no. 1, pp. 13–17, 2014.
    [19]
    G. M. Nielson. Smooth Interpolation of Orientations. Models and Techniques in Computer Animation, N. M. Thalmann, D. Thalmann, Eds., Tokyo, Japan: Springer, pp. 75−93, 1993. DOI: 10.1007/978-4-431-66911-1_8.
    [20]
    S. L. Zhang, S. Wang, F. S. Jing, M. Tan. A Sensorless hand guiding scheme based on model identification and control for industrial robot. IEEE Transactions on Industrial Informatics, vol. 15, no. 9, pp. 5204–5213, 2019. DOI: 10.1109/TII.2019.2900119.
    [21]
    S. L. Zhang, S. Wang, F. S. Jing, M. Tan. Parameter estimation survey for multi-joint robot dynamic calibration case study. Science China Information Sciences, vol. 62, no. 10, Article number 202203, 2019. DOI: 10.1007/s11432-018-9726-3.
  • 加载中

Catalog

    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(14)  / Tables(3)

    Article Metrics

    Article views (51) PDF downloads(76) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return