ISSN 0253-2778

CN 34-1054/N

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Bidirectional RRT algorithm based grasping manipulation of humanoid robots

  • 1.

    CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System, University of Science and Technology of China, Hefei 230027, China

  • 2.

    Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2016.01.003
  • Received Date: 19 May 2015
  • Accepted Date: 05 November 2015
  • Rev Recd Date: 05 November 2015
  • Publish Date: 30 January 2016
    Abstract

  • Abstract

    To realize grasping manipulation effectively in practical application, whole-body motion planning should be designed for humanoid robots. Thus, degrees of freedom of all the joints in humanoid robots, and constraints of robots, environment and the physical characteristics of grasped objects should be taken into consideration. To solve the problems including multi degrees of freedom and complex constraints, a new planning method is designed by using bidirectional RRT algorithm. After receiving stable double-leg configurations and the list of grasping hand’s poses, the bidirectional RRT algorithm is adopted to realize the whole-body motion planning for humanoid robots. Some experiments are conducted to make a NAO humanoid robot to open a drawer, enables to open a drawer in the presence of obstacles, and open a drawer for taking an object, and close the drawer. The results indicate that the whole-body motion planning with bidirectional RRT algorithm is effective in achieving the grasping manipulation of humanoid robots.

    Abstract

    To realize grasping manipulation effectively in practical application, whole-body motion planning should be designed for humanoid robots. Thus, degrees of freedom of all the joints in humanoid robots, and constraints of robots, environment and the physical characteristics of grasped objects should be taken into consideration. To solve the problems including multi degrees of freedom and complex constraints, a new planning method is designed by using bidirectional RRT algorithm. After receiving stable double-leg configurations and the list of grasping hand’s poses, the bidirectional RRT algorithm is adopted to realize the whole-body motion planning for humanoid robots. Some experiments are conducted to make a NAO humanoid robot to open a drawer, enables to open a drawer in the presence of obstacles, and open a drawer for taking an object, and close the drawer. The results indicate that the whole-body motion planning with bidirectional RRT algorithm is effective in achieving the grasping manipulation of humanoid robots.
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    • References(11)
  • [1]
    Tsuji T, Harada K, Kaneko K, et al. Selecting a suitable grasp motion for humanoid robots with a multi-fingered hand[C]// IEEE-RAS International Conference on Humanoid Robots. Daejeon, Korea: IEEE Press, 2008: 54-60.
    [2]
    Dalibard S, Nakhaei A, Lamiraux F, et al. Whole-body task planning for a humanoid robot: A way to integrate collision avoidance[C]// IEEE-RAS International Conference on Humanoid Robots. Paris, France: IEEE Press, 2009: 355-360.
    [3]
    Stilman M. Global manipulation planning in robot joint space with task constraints[J]. IEEE Transactions on Robotics and Automation, 2010, 26(3): 576-584.
    [4]
    Kavraki L E, Svestka P, Latombe J C, et al. Probabilistic roadmaps for path planning in high-dimensional configuration spaces[J]. IEEE Transactions on Robotics and Automation, 1996, 12(4): 566-580.
    [5]
    LaValle S M. Rapidly-exploring random trees: A new tool for path planning[R]. Department of Computer Science, Iowa State University, Ames, Technique Report 98-11, 1998.
    [6]
    LaValle S M, Kuffner J. Rapidly-exploring random trees: Progress and prospects[C]// Proceedings of Workshop on Algorithmic Foundations of Robotics. 2000: 293-308.
    [7]
    Kuffner J J, Lavalle S M. RRT-Connect: An efficient approach to single-query path planning[C]// Proceedings of the IEEE International Conference on Robotics & Automation. San Francisco, USA: IEEE Press, 2000, 2: 995-1001.
    [8]
    Kuffner J, Nishiwaki K, Kagami S, et al. Motion planning for humanoid robots under obstacle and dynamic balance constraints[C]// Proceedings of the IEEE International Conference on Robotics & Automation. Albuquerque, New Mexico: IEEE Press, 2001: 692-698.
    [9]
    Burget F, Hornung A, Bennewitz M. Whole-body motion planning for manipulation of articulated objects[C]// Proceedings of the IEEE International Conference on Robotics & Automation. Karsruhe, Germany: IEEE Press, 2013: 1656-1662.
    [10]
    LaValle S M. Planning Algorithms[M]. Cambridge, UK: Cambridge University Press, 2006.
    [11]
    王维,李焱. 基于RRT的虚拟人双臂操控规划方法[J]. 系统仿真学报, 2009, 21(20): 6515-6518.
    Wang Wei, Li Yan. RRT-based manipulation planning method for both arms of virtual human[J]. Journal of System Simulation, 2009, 21(20): 6515-6518.)
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    [1]
    Tsuji T, Harada K, Kaneko K, et al. Selecting a suitable grasp motion for humanoid robots with a multi-fingered hand[C]// IEEE-RAS International Conference on Humanoid Robots. Daejeon, Korea: IEEE Press, 2008: 54-60.
    [2]
    Dalibard S, Nakhaei A, Lamiraux F, et al. Whole-body task planning for a humanoid robot: A way to integrate collision avoidance[C]// IEEE-RAS International Conference on Humanoid Robots. Paris, France: IEEE Press, 2009: 355-360.
    [3]
    Stilman M. Global manipulation planning in robot joint space with task constraints[J]. IEEE Transactions on Robotics and Automation, 2010, 26(3): 576-584.
    [4]
    Kavraki L E, Svestka P, Latombe J C, et al. Probabilistic roadmaps for path planning in high-dimensional configuration spaces[J]. IEEE Transactions on Robotics and Automation, 1996, 12(4): 566-580.
    [5]
    LaValle S M. Rapidly-exploring random trees: A new tool for path planning[R]. Department of Computer Science, Iowa State University, Ames, Technique Report 98-11, 1998.
    [6]
    LaValle S M, Kuffner J. Rapidly-exploring random trees: Progress and prospects[C]// Proceedings of Workshop on Algorithmic Foundations of Robotics. 2000: 293-308.
    [7]
    Kuffner J J, Lavalle S M. RRT-Connect: An efficient approach to single-query path planning[C]// Proceedings of the IEEE International Conference on Robotics & Automation. San Francisco, USA: IEEE Press, 2000, 2: 995-1001.
    [8]
    Kuffner J, Nishiwaki K, Kagami S, et al. Motion planning for humanoid robots under obstacle and dynamic balance constraints[C]// Proceedings of the IEEE International Conference on Robotics & Automation. Albuquerque, New Mexico: IEEE Press, 2001: 692-698.
    [9]
    Burget F, Hornung A, Bennewitz M. Whole-body motion planning for manipulation of articulated objects[C]// Proceedings of the IEEE International Conference on Robotics & Automation. Karsruhe, Germany: IEEE Press, 2013: 1656-1662.
    [10]
    LaValle S M. Planning Algorithms[M]. Cambridge, UK: Cambridge University Press, 2006.
    [11]
    王维,李焱. 基于RRT的虚拟人双臂操控规划方法[J]. 系统仿真学报, 2009, 21(20): 6515-6518.
    Wang Wei, Li Yan. RRT-based manipulation planning method for both arms of virtual human[J]. Journal of System Simulation, 2009, 21(20): 6515-6518.)
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