Autonomous Robots

, Volume 33, Issue 4, pp 381–398 | Cite as

Force feedback exploiting tactile and proximal force/torque sensing

Theory and implementation on the humanoid robot iCub
  • Matteo Fumagalli
  • Serena Ivaldi
  • Marco Randazzo
  • Lorenzo Natale
  • Giorgio Metta
  • Giulio Sandini
  • Francesco Nori
Article

Abstract

The paper addresses the problem of measuring whole-body dynamics for a multiple-branch kinematic chain in presence of unknown external wrenches. The main result of the paper is to give a methodology for computing whole body dynamics by aligning a model of the system dynamics with the measurements coming from the available sensors. Three primary sources of information are exploited: (1) embedded force/torque sensors, (2) embedded inertial sensors, (3) distributed tactile sensors (i.e. artificial skin). In order to cope with external wrenches applied at continuously changing locations, we model the kinematic chain with a graph which dynamically adapts to the contact locations. Classical pre-order and post-order traversals of this dynamically evolving graph allow computing whole-body dynamics and estimate external wrenches. Theoretical results have been implemented in an open-source software library (iDyn) released under the iCub project. Experimental results on the iCub humanoid robot show the effectiveness of the proposed approach.

Keywords

Active force control Proximal sensing Multi-body dynamics 

References

  1. Caccavale, F., Natale, C., Siciliano, B., & Villani, L. (2005). Integration for the next generation: embedding force control into industrial robots. IEEE Robotics & Automation Magazine, 12(3), 53–64. CrossRefGoogle Scholar
  2. Calinon, S., Sardellitti, I., & Caldwell, D. (2010). Learning-based control strategy for safe human-robot interaction exploiting task and robot redundancies. In IEEE/RSJ int. conf. on intelligent robots and systems, Taipei, Taiwan. Google Scholar
  3. Cannata, G., Maggiali, M., Metta, G., & Sandini, G. (2008). An embedded artificial skin for humanoid robots. In IEEE int. conf. on multisensor fusion and integration, Seoul, Korea. Google Scholar
  4. Chiaverini, S., Siciliano, B., & Villani, L. (1999). A survey of robot interaction control schemes with experimental comparison. IEEE/ASME Transactions on Mechatronics, 4(3), 273–285. CrossRefGoogle Scholar
  5. Cormen, T., Leiserson, C., Rivest, R., & Stein, C. (2002). Introduction to algorithms (2nd ed.). New York: McGraw-Hill. Google Scholar
  6. Degallier, S., Righetti, L., Natale, L., Nori, F., Metta, G., & Ijspeert, A. (2008). A modular bio-inspired architecture for movement generation for the infant-like robot icub. In IEEE RAS/EMBS int. conf. on biomedical robotics and biomechatronics, Scottsdale, Arizona. Google Scholar
  7. Eiberger, O., Haddadin, S., Weis, M., Albu-Schäffer, A., & Hirzinger, G. (2010). On joint design with intrinsic variable compliance: derivation of the DLR QA-joint. In IEEE int. conf. on robotics and automation (pp. 1687–1694). CrossRefGoogle Scholar
  8. Featherstone, R. (2007). Rigid body dynamics algorithms. New York: Springer. Google Scholar
  9. Featherstone, R. (2010). Exploiting sparsity in operational-space dynamics. The International Journal of Robotics Research, 29, 1353–1368. CrossRefGoogle Scholar
  10. Featherstone, R., & Orin, D. E. (2008). Dynamics. In B. Siciliano & O. Khatib (Eds.), Handbook of robotics (pp. 35–65). Berlin: Springer. CrossRefGoogle Scholar
  11. Fitzpatrick, P., Metta, G., & Natale, L. (2008). Towards long-lived robot genes. Robotics and Autonomous Systems, 56, 29–45. CrossRefGoogle Scholar
  12. Fitzpatrick, P., Natale, L., & Metta, G. (2010). The Cmaking of a humanoid. The Kitware Source: Software Developer’s Quarterly, 13, 7–9. Google Scholar
  13. Fumagalli, M., Gijsberts, A., Ivaldi, S., Jamone, L., Metta, G., Natale, L., Nori, F., & Sandini, G. (2010a). Learning to exploit proximal force sensing: a comparison approach. In O. Sigaud & J. Peters (Eds.), From motor learning to interaction learning in robots (pp. 149–167). Berlin: Springer. CrossRefGoogle Scholar
  14. Fumagalli, M., Randazzo, M., Nori, F., Natale, L., Metta, G., & Sandini, G. (2010b). Exploiting proximal F/T measurements for the iCub active compliance. In IEEE/RSJ int. conf. on intelligent robots and systems, Taipei, Taiwan. Google Scholar
  15. Haddadin, S., Albu-Schäffer, A., Frommberger, M., & Hirzinger, G. (2008a). The role of the robot mass and velocity in physical human-robot interaction—part II: Constrained blunt impacts. In IEEE int. conf. on robotics and automation, Pasadena, CA, USA. Google Scholar
  16. Haddadin, S., Albu-Schäffer, A., & Hirzinger, G. (2008b). The role of the robot mass and velocity in physical human-robot interaction—part I: Non-constrained blunt impacts. In IEEE int. conf. on robotics and automation, Pasadena, CA, USA. Google Scholar
  17. Haddadin, S., Albu-Schaffer, A., Eiberger, O., & Hirzinger, G. (2010a). New insights concerning intrinsic joint elasticity for safety. In IEEE/RSJ int. conf. on intelligent robots and systems. Google Scholar
  18. Haddadin, S., Urbanek, H., Parusel, S., Burschka, D., Rossmann, J., Albu-Schaffer, A., & Hirzinger, G. (2010b). Real-time reactive motion generation based on variable attractor dynamics and shaped velocities. In 2010 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 3109–3116). CrossRefGoogle Scholar
  19. iCub Project, T. (2011). Documentation of icub kinematics. http://eris.liralab.it/wiki/ICubForwardKinematics.
  20. Ivaldi, S., Fumagalli, M., Randazzo, M., Nori, F., Metta, G., & Sandini, G. (2011). Computing robot internal/external wrenches by means of inertial, tactile and F/T sensors: theory and implementation on the iCub. In IEEE-RAS int. conf. humanoid robots (HUMANOIDS) (pp. 521–528). Google Scholar
  21. Ivaldi, S., Fumagalli, M., & Pattacini, U. (2011). Doxygen documentation of the iDyn library. http://eris.liralab.it/iCub/main/dox/html/group__iDyn.html.
  22. Janabi-Sharifi, F., Hayward, V., & Chen, C. S. J. (2000). Discrete-time adaptive windowing for velocity estimation. IEEE Transactions on Control Systems Technology, 8(6), 1003–1009. CrossRefGoogle Scholar
  23. Kulic, D., & Croft, E. (2007). Pre-collision safety strategies for human-robot interaction. Autonomous Robots, 22, 149–164. CrossRefGoogle Scholar
  24. Luca, A. D. (2006). Collision detection and safe reaction with the DLR-III lightweight manipulator arm. In IEEE/RSJ international conference on intelligent robots and systems (pp. 1623–1630). CrossRefGoogle Scholar
  25. Luh, J., Fisher, W., & Paul, R. (1983). Joint torque control by a direct feedback for industrial robots. IEEE Transactions on Automatic Control, 28(2), 153–161. MATHCrossRefGoogle Scholar
  26. Maggiali, M., Cannata, G., Maiolino, P., Metta, G., Randazzo, M., & Sandini, G. (2008). Embedded distributed capacitive tactile sensor. In Mechatronics 2008, Limerick, Ireland. Google Scholar
  27. Metta, G., Sandini, G., Vernon, D., Natale, L., & Nori, F. (2008). The iCub humanoid robot: an open platform for research in embodied cognition. In PerMIS: performance metrics for intelligent systems workshop, Washington DC, USA, Aug. 19–21. Google Scholar
  28. Metta, G., Natale, L., Nori, F., Sandini, G., Vernon, D., Fadiga, L., von Hofsten, C., Rosander, K., Santos-Victor, J., Bernardino, A., & Montesano, L. (2010). The iCub humanoid robot: an open-systems platform for research in cognitive development. Neural Networks, 23, 1125–1134, Special issue on social cognition: from babies to robots. CrossRefGoogle Scholar
  29. Minguez, J., Lamiraux, F., & Laumond, J. P. (2008). Motion planning and obstacle avoidance. In B. Siciliano & O. Khatib (Eds.), Handbook of robotics (pp. 827–852). Berlin: Springer. CrossRefGoogle Scholar
  30. Mistry, M., Buchli, J., & Schaal, S. (2010). Inverse dynamics control of floating base systems using orthogonal decomposition. In IEEE int. conf. on robotics and automation. Google Scholar
  31. Morel, G., & Dubowsky, S. (1996). The precise control of manipulators with joint friction: a base force/torque sensor method. In IEEE int. conf. on robotics and automation (pp. 360–365). Google Scholar
  32. Morel, G., Iagnemma, K., & Dubowsky, S. (2000). The precise control of manipulators with high joint friction using base force/torque sensing. Automatica, 36(7), 931–941. MathSciNetMATHCrossRefGoogle Scholar
  33. Mutambara, A. (1998). Decentralized estimation and control for multisensory systems. Boca Raton: CRC Press. Google Scholar
  34. Parmiggiani, A., Randazzo, M., Natale, L., Metta, G., & Sandini, G. (2009). Joint torque sensing for the upper-body of the iCub humanoid robot. In Int. conf. on humanoid robotics (p. 2009). France: Paris. Google Scholar
  35. Pattacini, U. (2011). Doxygen documentation of the iKyn library. http://eris.liralab.it/iCub/main/dox/html/group__iKin.html.
  36. Pattacini, U., Nori, F., Natale, L., Metta, G., & Sandini, G. (2010). An experimental evaluation of a novel minimum-jerk Cartesian controller for humanoid robots. In IEEE/RSJ int. conf. on intelligent robots and systems (IROS), Taipei, Taiwan. Google Scholar
  37. Pratt, G., & Williamson, M. (1995). Series elastic actuators. In IEEE/RSJ int. conf. on intelligent robots and systems, Los Alamitos, CA, USA (pp. 399–406). Google Scholar
  38. Randazzo, M., Fumagalli, M., Nori, F., Natale, L., Metta, G., & Sandini, G. (2011). A comparison between joint level torque sensing and proximal F/T sensor torque estimation: implementation on the iCub. In IEEE-RSJ int. conf on intelligent robots and systems (IROS), San Francisco, USA. Google Scholar
  39. Roboskin, E. P. I. F. (2010). http://www.roboskin.eu.
  40. Santis, A. D., Siciliano, B., Deluca, A., & Bicchi, A. (2008). An atlas of physical human-robot interaction. Mechanism and Machine Theory, 43(3), 253–270. MATHCrossRefGoogle Scholar
  41. Sciavicco, L., & Siciliano, B. (2005). Advanced textbooks in control and signal processing. Modelling and control of robot manipulators (2nd ed.). Berlin: Springer. Google Scholar
  42. Siciliano, B., & Villani, L. (1996). A passivity-based approach to force regulation and motion control of robot manipulators. Automatica, 32(3), 443–447. MathSciNetMATHCrossRefGoogle Scholar
  43. Siciliano, B., & Villani, L. (2000). Robot force control. Norwell: Kluwer Academic. CrossRefGoogle Scholar
  44. Sisbot, E., Marin-Urias, L., Broquère, X., Sidobre, D., & Alami, R. (2010). Synthesizing robot motions adapted to human presence. International Journal of Social Robotics, 2, 329–343. CrossRefGoogle Scholar
  45. Wittenburg, J. (1994). Topological description of articulated systems. In NATO ASI series: Vol. 268. Computer-aided analysis of rigid and flexible mechanical systems, Part I (pp. 159–196). http://www.springerlink.com/content/l364j72l49103vv2/. CrossRefGoogle Scholar
  46. Xsens (2012). The MTx orientation tracker user manual. http://www.xsens.com/en/general/mtx.

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Matteo Fumagalli
    • 1
    • 2
  • Serena Ivaldi
    • 1
    • 3
  • Marco Randazzo
    • 1
  • Lorenzo Natale
    • 1
  • Giorgio Metta
    • 1
    • 4
  • Giulio Sandini
    • 1
    • 4
  • Francesco Nori
    • 1
  1. 1.Department of Robotics Brain and Cognitive SciencesIstituto Italiano di TecnologiaGenovaItaly
  2. 2.Department of Control EngineeringUniversity of TwenteEnschedeThe Netherlands
  3. 3.Institut des Systemes Intelligents et de RobotiqueUniversity Pierre et Marie CurieParisFrance
  4. 4.Department of Informatics, Systems and Communications (DIST)University of GenovaGenovaItaly

Personalised recommendations