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Grasp and Motion Planning for Humanoid Robots

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Grasping in Robotics

Part of the book series: Mechanisms and Machine Science ((Mechan. Machine Science,volume 10))

Abstract

The capability of humanoid robots to grasp objects is a key competence for their successful application in human-centered environments. We present an approach for grasping daily objects consisting of offline and online phases for grasp and collision-free motion planning. The proposed method generates object-related sets of feasible grasping configurations in an offline phase that are being used for online planning of grasping motions on a humanoid robot. Generating force-closure (FC) grasps on 3D objects is considered to be a hard problem, since many parameters, such as hand kinematics, object geometry, material properties, and forces have to be taken into account, making the space of possible candidate grasps too large to search exhaustively. We believe that the key to find stable grasps in an efficient manner is to use a special representation of the object geometry that can be easily analyzed. In this chapter, we present a novel grasp planning method that evaluates local symmetry properties of objects to generate only candidate grasps that are likely to be of good quality. We achieve this by computing the medial axis which represents symmetry properties of 3D objects by inscribing spheres of maximum diameter into the original shape. Our grasp planner performs offline analysis of the object’s medial axis and generates geometrically meaningful candidate grasps. These are then tested for FC in order to create sets of feasible grasps. The resulting grasp sets are used during the online phase for planning collision-free grasping motions with the IK-RRT approach. In contrast to classical motion planning algorithms related to Rapidly exploring Random Trees (RRT), the IK-RRT planner does not rely on one specific goal configuration, but it implicitly uses a goal region in configuration space that is implied by a set of potential grasping configurations in workspace. By using efficient IK-solvers to sample potential goal configurations during planning, the IK-RRT planner is able to efficiently compute collision-free grasping motions for high-dimensional planning problems. Further, an extension to bimanual grasping problems is discussed and evaluations on the humanoid robot ARMAR-III [3] are performed.

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Notes

  1. 1.

    These performance evaluations have been carried out on a DualCore system with 2.0 GHz.

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Authors and Affiliations

  1. Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT), Adenauerring 2, 76131, Karlsruhe, Germany

    Markus Przybylski, Nikolaus Vahrenkamp, Tamim Asfour & Rüdiger Dillmann

Authors
  1. Markus Przybylski
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  2. Nikolaus Vahrenkamp
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  3. Tamim Asfour
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  4. Rüdiger Dillmann
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Corresponding author

Correspondence to Markus Przybylski .

Editor information

Editors and Affiliations

  1. University of Cassino, Via G. Di Biasio 43, Cassino, 03043, Italy

    Giuseppe Carbone

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Przybylski, M., Vahrenkamp, N., Asfour, T., Dillmann, R. (2013). Grasp and Motion Planning for Humanoid Robots . In: Carbone, G. (eds) Grasping in Robotics. Mechanisms and Machine Science, vol 10. Springer, London. https://doi.org/10.1007/978-1-4471-4664-3_13

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  • DOI: https://doi.org/10.1007/978-1-4471-4664-3_13

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