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

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  • 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
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    References listed on IDEAS

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    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.
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