IDEAS home Printed from https://ideas.repec.org/a/taf/tsysxx/v47y2016i4p932-945.html
   My bibliography  Save this article

Dynamic design, numerical solution and effective verification of acceleration-level obstacle-avoidance scheme for robot manipulators

Author

Listed:
  • Lin Xiao
  • Yunong Zhang

Abstract

For avoiding obstacles and joint physical constraints of robot manipulators, this paper proposes and investigates a novel obstacle avoidance scheme (termed the acceleration-level obstacle-avoidance scheme). The scheme is based on a new obstacle-avoidance criterion that is designed by using the gradient neural network approach for the first time. In addition, joint physical constraints such as joint-angle limits, joint-velocity limits and joint-acceleration limits are incorporated into such a scheme, which is further reformulated as a quadratic programming (QP). Two important ‘bridge’ theorems are established so that such a QP can be converted equivalently to a linear variational inequality and then equivalently to a piecewise-linear projection equation (PLPE). A numerical algorithm based on a PLPE is thus developed and applied for an online solution of the resultant QP. Four path-tracking tasks based on the PA10 robot in the presence of point and window-shaped obstacles demonstrate and verify the effectiveness and accuracy of the acceleration-level obstacle-avoidance scheme. Besides, the comparisons between the non-obstacle-avoidance and obstacle-avoidance results further validate the superiority of the proposed scheme.

Suggested Citation

  • Lin Xiao & Yunong Zhang, 2016. "Dynamic design, numerical solution and effective verification of acceleration-level obstacle-avoidance scheme for robot manipulators," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(4), pages 932-945, March.
    • Handle: RePEc:taf:tsysxx:v:47:y:2016:i:4:p:932-945
    DOI: 10.1080/00207721.2014.909971
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207721.2014.909971
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207721.2014.909971?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mohammad Fateh & Hojjat Tehrani & Seyed Karbassi, 2013. "Repetitive control of electrically driven robot manipulators," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(4), pages 775-785.
    2. Jianan Wang & Ming Xin, 2013. "Optimal consensus algorithm integrated with obstacle avoidance," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(1), pages 166-177.
    3. M.K. Singh & D.R. Parhi, 2011. "Path optimisation of a mobile robot using an artificial neural network controller," International Journal of Systems Science, Taylor & Francis Journals, vol. 42(1), pages 107-120.
    4. Wen Yu & Kang Li & Xiaoou Li, 2011. "Automated nonlinear system modelling with multiple neural networks," International Journal of Systems Science, Taylor & Francis Journals, vol. 42(10), pages 1683-1695.
    5. T. Periasamy & T. Asokan & M. Singaperumal, 2012. "Investigations on the dynamic coupling in AUV-manipulator system and the manipulator trajectory errors using bond graph method," International Journal of Systems Science, Taylor & Francis Journals, vol. 43(6), pages 1104-1122.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liguo Weng & Min Xia & Wei Wang & Qingshan Liu, 2015. "Crew exploration vehicle (CEV) attitude control using a neural–immunology/memory network," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(1), pages 152-158, January.
    2. Gabriel G. R. de Castro & Guido S. Berger & Alvaro Cantieri & Marco Teixeira & José Lima & Ana I. Pereira & Milena F. Pinto, 2023. "Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs," Agriculture, MDPI, vol. 13(2), pages 1-25, January.
    3. Lan Zhou & Jinhua She & Min Wu, 2015. "A one-step method of designing an observer-based modified repetitive-control system," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(14), pages 2617-2627, October.
    4. Kuangrong Hao & Chongbin Guo & Yongsheng Ding, 2014. "Physiological hemostasis based intelligent integrated cooperative controller for precise fault-tolerant control of redundant parallel manipulator," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(10), pages 2072-2087, October.
    5. Pedro V. S. G. de Lima & Rafael C. Neto & Francisco A. S. Neves & Fabrício Bradaschia & Helber E. P. de Souza & Eduardo J. Barbosa, 2023. "Zero-Phase FIR Filter Design Algorithm for Repetitive Controllers," Energies, MDPI, vol. 16(5), pages 1-33, March.
    6. Carlos Aguilar-Avelar & Javier Moreno-Valenzuela, 2017. "A MRAC Principle for a Single-Link Electrically Driven Robot with Parameter Uncertainties," Complexity, Hindawi, vol. 2017, pages 1-13, January.
    7. Lan Zhou & Jinhua She, 2015. "Design of a robust output-feedback-based modified repetitive-control system," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(5), pages 808-817, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:tsysxx:v:47:y:2016:i:4:p:932-945. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TSYS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.