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Autonomous Robots

, Volume 38, Issue 1, pp 17–30 | Cite as

Representing the robot’s workspace through constrained manipulability analysis

  • Nikolaus Vahrenkamp
  • Tamim Asfour
Article

Abstract

Quantifying the robot’s performance in terms of dexterity and maneuverability is essential for the analysis and design of novel robot mechanisms and for the selection of appropriate robot configurations in the context of grasping and manipulation. It can also be used for monitoring and evaluating the current robot state and support planning and decision making tasks, such as grasp selection or inverse kinematics (IK) computation. To this end, we propose an extension to the well-known Yoshikawa manipulability ellipsoid measure Yoshikawa (Int J Robotics Res 4(2):3–9, 1985), which incorporates constraining factors, such as joint limits or the self-distance between manipulator and other parts of the robot. Based on this measure we show how an extended capability representation of the robot’s workspace can be built in order to support online queries like grasp selection or inverse kinematics solving. In addition to single handed grasping tasks, we discuss how the approach can be extended to bimanual grasping tasks. The proposed approaches are evaluated in simulation and we show how the extended manipulability measure is used within the grasping and manipulation pipeline of the humanoid robot ARMAR-III.

Keywords

Manipulability Reachability  Redundant manipulators Capability representation 

Notes

Acknowledgments

The research leading to these results has received funding from the European Union Seventh Framework Programme under Grant agreement No 611832 (WALK-MAN) and Grant agreement No 611909 (KoroiBot).

References

  1. Abdel-Malek, K., Yu, W., & Yang, J. (2004). Placement of robot manipulators to maximize dexterity. International Journal of Robotics and Automation, 19(1), 6–14.CrossRefGoogle Scholar
  2. Asfour, T. Regenstein, K. Azad, P. Schröder, J. Bierbaum, A. Vahrenkamp, N. & Dillmann, R. (2006). Armar-III: An integrated humanoid platform for sensory-motor control. In IEEE-RAS International Conference on Humanoid Robots (Humanoids 2006), pp. 169–175.Google Scholar
  3. Asfour, T. Schill, J. Peters, H., Klas, C. Bücker, J. Sander, C. Schulz, S. Kargov, A. Werner, T. & Bartenbach, V. (2013). ARMAR-4: A 63 DOF torque controlled humanoid robot. In IEEE/RAS International Conference on Humanoid Robots (Humanoids).Google Scholar
  4. Azad, P. Asfour, T. & Dillmann, R. (2009). Accurate shape-based 6-dof pose estimation of single-colored objects. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2690–2695.Google Scholar
  5. Azad, P. Asfour, T. & Dillmann, R. (2009). Combining harris interest points and the sift descriptor for fast scale-invariant object recognition. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4275–4280.Google Scholar
  6. Bicchi, A. Prattichizzo, D. & Melchiorri, C. (1997). Force and dynamic manipulability for cooperating robot systems. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1479–1484.Google Scholar
  7. Chan, T. F., & Dubey, R. (1995). A weighted least-norm solution based scheme for avoiding joint limits for redundant joint manipulators. IEEE Transactions on Robotics and Automation, 11(2), 286–292.CrossRefGoogle Scholar
  8. Chen, H., Lee, S. I., Do, J. H., & Lee, J. M. (2013). Directional manipulability to improve performance index of dual arm manipulator for object grasping. In S. Lee, H. Cho, K. J. Yoon, & J. Lee (Eds.), intelligent autonomous systems 12, advances in intelligent systems and computing (Vol. 193, pp. 595–602). Berlin Heidelberg: Springer.CrossRefGoogle Scholar
  9. Chiacchio, P., Chiaverini, S., Sciavicco, L., & Siciliano, B. (1991). Global task space manipulability ellipsoids for multiple-arm systems. IEEE Transactions on Robotics and Automation, 7(5), 678–685.CrossRefGoogle Scholar
  10. Dariush, B. Bin Hammam, G. & Orin, D. (2010). Constrained resolved acceleration control for humanoids. In IEEE/RSJ International Conference on Intelligent Robots and Systems 2010, pp. 710–717.Google Scholar
  11. Diankov, R. (2010). Automated construction of robotic manipulation programs. Ph.D. thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA.Google Scholar
  12. Finotello, R. Grasso, T. Rossi, G. & Terribile, A. (1998). Computation of kinetostatic performances of robot manipulators with polytopes. In Proceedings of the IEEE International Conference on Robotics and Automation, vol. 4, pp. 3241–3246.Google Scholar
  13. Flacco, F. Luca, A. D. & Khatib, O. (2012). Prioritized multi-task motion control of redundant robots under hard joint constraints. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), Algarve, pp. 3970–3977.Google Scholar
  14. Jamone, L. Natale, L. Metta, G. Nori, F. & Sandini, G. (2012). Autonomous online learning of reaching behavior in a humanoid robot. International Journal of Humanoid Robotics, 09(03), 1–26.Google Scholar
  15. Jamone, L., Brandao, M., Natale, L., Hashimoto, K., Sandini, G., & Takanishi, A. (2014). Autonomous online generation of a motor representation of the workspace for intelligent whole-body reaching. Robotics and Autonomous Systems, 62(04), 556–567.CrossRefGoogle Scholar
  16. Kokkinis, T. & Paden, B. (1989). Kinetostatic performance limits of cooperating robot manipulators using force-velocity polytopes. In ASME Annual Meeting, Rootics, pp. 151–155.Google Scholar
  17. Lee, J. (1997). A study on the manipulability measures for robot manipulators. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems IROS ’97, vol. 3, pp. 1458–1465.Google Scholar
  18. Lee, S. (1989). Dual redundant arm configuration optimization with task-oriented dual arm manipulability. IEEE Transactions on Robotics and Automation, 5(1), 78–97.CrossRefGoogle Scholar
  19. Madhani, A., & Dubowsky, S. (1997). The force workspace: A tool for the design and motion planning of multi-limb robotic systems. ASME Journal of Mechanical Design, 119, 218–224.CrossRefGoogle Scholar
  20. Merlet, J. (2007). Jacobian, manipulability, condition number and accuracy of parallel robots. In S. Thrun, R. Brooks, & H. Durrant-Whyte (Eds.), Robotics Research, Springer Tracts in Advanced Robotics (Vol. 28, pp. 175–184). Berlin /Heidelberg: Springer.Google Scholar
  21. Nait-Chabane, K. Hoppenot, P. & Colle, E. (2007). Directional manipulability for motion coordination of an assistive mobile arm. In ICINCO 2007, Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics, pp. 201–208.Google Scholar
  22. Nakamura, Y. Nagai, K. & Yoshikawa, T. (1987). Mechanics of coordinative manipulation by multiple robotic mechanisms. In Proceedings of the IEEE International Conference on Robotics and Automation 1987, vol. 4, pp. 991–998.Google Scholar
  23. Prattichizzo, D., & Trinkle, J. C. (2008). Grasping. Handbook of robotics (pp. 671–700). Heidelberg: Springer.CrossRefGoogle Scholar
  24. Przybylski, M. Asfour, T. & Dillmann, R. (2011). Planning grasps for robotic hands using a novel object representation based on the medial axis transform. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 1781–1788.Google Scholar
  25. Porges, O., Stouraitis, T., Borst, C., & Roa, M. (2014). Reachability and capability analysis for manipulation tasks. Advances in intelligent systems and computing (pp. 703–718). New York: Springer International Publishing.Google Scholar
  26. Siciliano, B., & Khatib, O. (Eds.). (2008). Springer handbook of robotic. Heidelberg: Springer.Google Scholar
  27. Ting-Yung Wen, J., & Wilfinger, L. (1999). Kinematic manipulability of general constrained rigid multibody systems. IEEE Transactions on Robotics and Automation, 15(3), 558–567.CrossRefGoogle Scholar
  28. Togai, M. (1986). An application of the singular value decomposition to manipulability and sensitivity of industrial robots. SIAM Journal on Algebraic and Discrete Methods, 7(2), 315–320.CrossRefzbMATHMathSciNetGoogle Scholar
  29. Tsai, M.J. (1986). Workspace geometric characterization and manipulability of industrial robots. Ph.D. thesis, Ohio State University.Google Scholar
  30. Ulbrich, S., Ruiz, V., Asfour, T., Torras, C., & Dillmann, R. (2012). Kinematic bezier maps. IEEE Transactions on Systems, Man, and Cybernetics, 42(4), 1215–1230.CrossRefGoogle Scholar
  31. Vahrenkamp, N. Asfour, T. & Dillmann, R. (2012). Efficient inverse kinematics computation based on reachability analysis. International Journal of Humanoid Robotics, 9(4).Google Scholar
  32. Vahrenkamp, N. Asfour, T. & Dillmann, R. (2013). Robot placement based on reachability inversion. In IEEE International Conference on Robotics and Automation (ICRA), pp. 1970–1975.Google Scholar
  33. Vahrenkamp, N. Asfour, T. Metta, G. Sandini, G. & Dillmann, R. (2012). Manipulability analysis. In Proceedings of IEEE-RAS International Conference on Humanoid Robots (Humanoids), Osaka, pp. 568–573.Google Scholar
  34. Vahrenkamp, N. Barski, A. Asfour, T. & Dillmann, R. (2009). Planning and execution of grasping motions on a humanoid robot. In 9th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2009, pp. 639–645.Google Scholar
  35. Vahrenkamp, N. Berenson, D. Asfour, T. Kuffner, J. & Dillmann, R. (2009). Humanoid motion planning for dual-arm manipulation and re-grasping tasks. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), St. Louis, pp. 2464–2470.Google Scholar
  36. Vahrenkamp, N. Kröhnert, M. Ulbrich, S. Asfour, T. Metta, G. Dillmann, R. & Sandini, G. (2012). Simox: A robotics toolbox for simulation, motion and grasp planning. In International Conference on Intelligent Autonomous Systems (IAS), pp. 1–25.Google Scholar
  37. Vahrenkamp, N. Wieland, S. Azad, P. Gonzalez, D. Asfour, T. & Dillmann, R. (2008). Visual servoing for humanoid grasping and manipulation tasks. In 8th IEEE-RAS International Conference on Humanoid Robots, 2008, Humanoids, pp. 406–412.Google Scholar
  38. Watanabe, T. (2011). Effect of torque-velocity relationship on manipulability for robot manipulators. Journal of Mechanisms and Robotics, 3(4), 041007.CrossRefGoogle Scholar
  39. Welke, K. Schiebener, D. Asfour, T. & Dillmann, R. (2013). Gaze selection during manipulation. In IEEE International Conference on Robotics and Automation (ICRA), pp. 652–659.Google Scholar
  40. Yoshikawa, T. (1985). Manipulability of robotic mechanisms. The International Journal of Robotics Research, 4(2), 3–9.Google Scholar
  41. Zacharias, F. Borst, C. & Hirzinger, G. (2007). Capturing robot workspace structure: Representing robot capabilities. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 3229–3236.Google Scholar
  42. Zacharias, F. Leidner, D. Schmidt, F. Borst, C. & Hirzinger, G. (2010). Exploiting structure in two-armed manipulation tasks for humanoid robots. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 5446–5452.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.High Performance Humanoid Technologies (H2T)Karlsruhe Institute of Technology (KIT)KarlsruheGermany

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