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Path planning for grasping operations using an adaptive PCA-based sampling method

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Abstract

The planning of collision-free paths for a hand-arm robotic system is a difficult issue due to the large number of degrees of freedom involved and the cluttered environment usually encountered near grasping configurations. To cope with this problem, this paper presents a novel importance sampling method based on the use of principal component analysis (PCA) to enlarge the probability of finding collision-free samples in these difficult regions of the configuration space with low clearance. By using collision-free samples near the goal, PCA is periodically applied in order to obtain a sampling volume near the goal that better covers the free space, improving the efficiency of sampling-based path planning methods. The approach has been tested with success on a hand-arm robotic system composed of a four-finger anthropomorphic mechanical hand (17 joints with 13 independent degrees of freedom) and an industrial robot (6 independent degrees of freedom).

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References

  • Berenson, D., Srinivasa, S., Ferguson, D., & Kuffner, J. (2009). Manipulation planning on constraint manifolds. In: Proceedings of the IEEE International Conference on Robotics and Automation (pp. 625–632).

  • Biagiotti, L., Lotti, F., Melchiorri, C., & Vassura, G. (2004). How far is the human hand? A review on anthropomorphic robotic end-effectors. Technical Reports: University of Bologna.

  • Bicchi, A. (2000). Hands for dexterous manipulation and robust grasping: A difficult road toward simplicity. IEEE Transactions on Robotics and Automation, 16(6), 652–662.

    Article  Google Scholar 

  • Blanchette, J., & Summerfield, M. (2006). C++ GUI Programming with Qt 4. Upper Saddle River, NJ, USA: Prentice Hall PTR.

    Google Scholar 

  • Boor, V., Overmars, M. H., & van der Stappen, A. F. (1999). The Gaussian sampling strategy for probabilistic roadmap planners. In: Proceedings of the IEEE International Conference on Robotics and Automation (pp. 1018–1023).

  • Choset, H., Lynch, K. M., Hutchinson, S., Kantor, G., Burgard, W., Kavraki, L. E., Thrun, S. (2005) Principles of Robot Motion. Cambridge: The MIT Press.

  • Ciocarlie, M. T., & Allen, P. K. (2009). Hand posture subspaces for dexterous robotic grasping. The International Journal of Robotics Research, 28(7), 851–867.

    Article  Google Scholar 

  • Dalibard, S., & Laumond, J. (2008). Control of probabilistic diffusion in motion planning. In: Proceedings of the Workshop on Algorithmic Foundations of Robotics (pp. 467–481)

  • Dalibard, S., & Laumond, J. (2011). Linear dimensionality reduction in random motion planning. The Intenational Journal of Robotics Research, 30(12), 1461–1476.

    Article  Google Scholar 

  • Denny, J., & Amato, N. (2012). A Coordinated Mapping of C- free and C-obatacle in Arbitrary Dimension. In E. Frazzoli, T. Lozano-Perez, N. Roy, & D. Rus (Eds.), Algorithmic Foundations of Robotics X, Springer Tracts in Advanced Robotics, Vol. 86. Berlin: Springer.

    Google Scholar 

  • Eaton, J. W. (2002). GNU Octave Manual. Bristol: Network Theory Limited.

    Google Scholar 

  • Eddelbuettel, D. (2011). Rcpp: Seamless R and C++ integration. http://dirk.eddelbuettel.com/code/rcpp.html. Accessed 9 April 2013.

  • Foundation R. (2011). The R project for statistical computing. http://www.r-project.org. Accessed 9 April 2013.

  • Gabiccini, M., Bicchi, A., Prattichizzo, D., & Malvezzi, M. (2011). On the role of hand synergies in the optimal choice of grasping forces. Autonomous Robots, 31, 235–252.

    Article  Google Scholar 

  • Geraerts, R., & Overmars, M. H. (2004). Sampling techniques for probabilistic roadmap planners. Intelligent Autonomous Systems, 8, 600–609.

    Google Scholar 

  • Gottschalk, S., Lin, M.C., Manocha, D. (1996). OBBtree: A hierarchical structure for rapid interference detection. In: Proceedings of the 23rd Annual Conference on Computer Graphics and interactive Techniques, ACM, New York, NY, USA, SIGGRAPH 1996 (pp 171180).

  • Hsiao, K., Pack-Kaelbling, L., & Lozano-Perez, T. (2011). Robust grasping under pose uncertainty. Autonomous Robots, 31, 253–268.

    Article  Google Scholar 

  • Hsu, D., Jiang, T., Reif, J., & Sun, Z. (2003). The bridge test for sampling narrow passages with probabilistic roadmap planners. In: Proceedings of the IEEE International Conference on Robotics and Automation (pp. 4420–4426).

  • Hsu, D., Sanchez-Ante, G., & Sun, Z. (2005). Hybrid PRM sampling with a cost-sensitive adaptive strategy. In: Proceedings of the IEEE International Conference on Robotics and Automation (pp. 3874–3880).

  • Hsu, D., Latombe, J. C., & Kurniawati, H. (2006). On the probabilistic foundations of probabilistic roadmap planning. The Intenational Journal of Robotics Research, 25(7), 627–643.

    Article  Google Scholar 

  • Jolliffe, I. (2002). Principal Component Analysis., Statistics New York: Springer.

  • Kavraki, L. E., Svestka, P., Latombe, J. C., & Overmars, M. K. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12(4), 566–580.

    Article  Google Scholar 

  • Kongsberg Oil & Gas Technologies. (2010). Coin3D–3D graphics development tools. www.coin3d.org. Accessed 9 April 2013.

  • Kuffner, J. (2004). Effective sampling and distance metrics for 3d rigid body path planning. In: Proceedings of International Conference on Robotics and Automation (pp. 3993–3998).

  • Larsen, E., Gottschalk, S., Lin, M. C., & Manocha, D. (2000). Fast proximity queries with swept sphere volumes. In: Proceedings of International Conference on Robotics and Automation (pp. 3719–3726).

  • Laschi, C., Asuni, G., Guglielmelli, E., Teti, G., Johansson, R., Konodu, H., et al. (2008). A bio-inspired predictive sensory-motor coordination scheme for robot reaching and preshaping. Autonomous Robots, 25, 85–101.

    Article  Google Scholar 

  • Pérez, A., Rosell, J. (2010). The Kautham Project. https://sir.upc.es/kautham/. Accessed 9 April 2013.

  • Pérez, A., Rosell, J. (2009). A Roadmap to Robot Motion Planning Software Development. Computer Applications in Engineering Education.

  • Rosell, J., Suárez, R., Rosales, C., García, J. A., & Pérez, A. (2009). Motion planning for high DOF anthropomorphic hands. In: Proceedings of the IEEE International Conference on Robotics and Automation (pp. 4025–4030).

  • Rosell, J., Cruz, L., Suárez, R., & Pérez, A. (2011a). Importance sampling based on adaptive principal component analysis. In: Proceedings of the IEEE International Symposium on Assembly and Manufacturing.

  • Rosell, J., Suárez, R., Rosales, C., & Pérez, A. (2011b). Autonomous motion planning of a hand-arm robotic system based on captured human-like hand postures. Autonomous Robots, 31, 87–102.

    Google Scholar 

  • Safonova, A., Hodgins, J. K., & Pollard, N. S. (2004). Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces. ACM Transactions Graph, 23(3), 514–521.

    Google Scholar 

  • Sanderson, C. (2010). Armadillo: An open source C++ linear algebra library for fast prototyping and computationally intensive experiments. Technical Report: NICTA

  • Santello, M., Flanders, M., Soechting, J. F. (1998). Postural hand synergies for tool use. Journal of Neuroscience 18(23):10105–10115.

    Google Scholar 

  • Stilman, M. (2010). Global manipulation planning in robot joint space with task constraints. IEEE Transactions on Robotics, 26(3), 576–584.

    Article  Google Scholar 

  • Suárez, R., Rosell, J., Pérez, A., & Rosales, C. (2009). Efficient search of obstacle-free paths for anthropomorphic hands. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 1773–1778).

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Acknowledgments

This work was partially supported by the Spanish Government through the projects DPI2010-15446, DPI2011-22471 and PI09/90088.

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Authors and Affiliations

  1. Institute of Industrial and Control Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain

    Jan Rosell, Raúl Suárez & Alexander Pérez

  2. Escuela Colombiana de Ingeniería “Julio Garavito”, Bogotá D.C., Colombia

    Alexander Pérez

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  1. Jan Rosell
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  2. Raúl Suárez
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  3. Alexander Pérez
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Correspondence to Jan Rosell.

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Rosell, J., Suárez, R. & Pérez, A. Path planning for grasping operations using an adaptive PCA-based sampling method. Auton Robot 35, 27–36 (2013). https://doi.org/10.1007/s10514-013-9332-5

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  • DOI: https://doi.org/10.1007/s10514-013-9332-5

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