Force and Visual Control for Safe Human-Robot Interaction

  • Bruno Siciliano
  • Luigi Villani
  • Vincenzo Lippiello
  • Agostino De Santis
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 83)


Abstract. Unlike the industrial robotics domain where the workspace of machines and humans can be segmented, applications of intelligent machines that work in contact with humans are increasing, which involve e.g. haptic interfaces and teleoperators, cooperative material-handling, power extenders and such high-volume markets as rehabilitation, physical training and entertainment. Force and vision play a fundamental role to increase the autonomy of a robotic system, especially in the presence of humans. Vision provides global information on the surrounding environment to be used for motion planning and obstacle avoidance, while force allows adjusting the robot motion so that the local constraints imposed by the environment are satisfied. In order to avoid dangerous collisions and ensure a safe interaction, suitable control strategies based on force and visual feedback can be used while tracking human motion. This paper surveys such strategies and presents some experimental results in a number of significant case studies.


Extend Kalman Filter Collision Avoidance Impedance Control Base Frame Camera Frame 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    De Santis, A., Siciliano, B., De Luca, A., Bicchi, A.: An atlas of physical Human–Robot Interaction. Mechanism and Machine Theory 43, 253–270 (2008)zbMATHCrossRefGoogle Scholar
  2. 2.
    Zinn, M., Khatib, O., Roth, O., Salisbury, J.K.: Playing it safe. IEEE Robotics and Automation Magazine 11(2), 12–21 (2004)CrossRefGoogle Scholar
  3. 3.
    Bicchi, A., Tonietti, G.: Fast and soft-arm tactics. IEEE Robotics and Automation Magazine 11(2), 22–33 (2004)CrossRefGoogle Scholar
  4. 4.
    Hashimoto, H.: Intelligent interactive spaces — Integration of IT and robotics. In: Proceedings of IEEE Workshop on Advanced Robotics and its Social Impacts, pp. 85–90 (2005)Google Scholar
  5. 5.
    Hosoda, K., Igarashi, K., Asada, M.: Adaptive hybrid control for visual and force servoing in an unknown environment. IEEE Robotics and Automation Magazine 5(4), 39–43 (1998)CrossRefGoogle Scholar
  6. 6.
    Nelson, B.J., Morrow, J.D., Khosla, P.K.: Improved force control through visual servoing. In: Proceedings of American Control Conference, pp. 380–386 (1995)Google Scholar
  7. 7.
    Baeten, J., De Schutter, J.: Integrated Visual Servoing and Force Control. The Task Frame Approach. Springer, Heidelberg (2004)Google Scholar
  8. 8.
    Morel, G., Malis, E., Boudet, S.: Impedance based combination of visual and force control. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 1743–1748 (1998)Google Scholar
  9. 9.
    Olsson, T., Johansson, R., Robertsson, A.: Flexible force-vision control for surface following using multiple cameras. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and System, pp. 798–803 (2004)Google Scholar
  10. 10.
    Siciliano, B., Villani, L.: Robot Force Control. Kluwer, Dordrecht (1999)zbMATHGoogle Scholar
  11. 11.
    Lippiello, V., Siciliano, B., Villani, L.: A position-based visual impedance control for robot manipulators. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 2068–2073 (2007)Google Scholar
  12. 12.
    Lippiello, V., Siciliano, B., Villani, L.: Robot force/position control with force and visual feedback. In: Proceedings of European Control Conference, pp. 3790–3795 (2007)Google Scholar
  13. 13.
    De Santis, A., Albu-Schaeffer, A., Ott, C., Siciliano, B., Hirzinger, G.: The skeleton algorithm for self-collision avoidance of a humanoid manipulator. In: Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics (2007)Google Scholar
  14. 14.
    Hirzinger, G., Albu-Schaeffer, A., Hahnle, M., Schaefer, I., Sporer, N.: On a new generation of torque controlled light-weight robots. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 3356–3363 (2001)Google Scholar
  15. 15.
    De Santis, A., Pierro, P., Siciliano, B.: The virtual end-effectors approach for human-robot interaction. In: Lenarčič, J., Roth, B. (eds.) Advances in Robot Kinematics. Springer, Heidelberg (2006)Google Scholar
  16. 16.
    Espiau, B., Chaumette, F., Rives, P.: A new approach to visual servoing in robotics. IEEE Transactions on Robotics and Automation 12, 313–326 (1996)CrossRefGoogle Scholar
  17. 17.
    Lippiello, V., Villani, L.: Managing redundant visual measurements for accurate pose tracking. Robotica 21, 511–519 (2003)CrossRefGoogle Scholar
  18. 18.
    Villani, L., De Schutter, J.: Force control. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics. Springer, Heidelberg (2008)Google Scholar
  19. 19.
    Lippiello, V., Siciliano, B., Villani, L.: Position-based visual servoing in industrial multi-robot cells using a hybrid camera configuration. IEEE Transactions on Robotics 23, 73–86 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Bruno Siciliano
    • 1
  • Luigi Villani
    • 1
  • Vincenzo Lippiello
    • 1
  • Agostino De Santis
    • 1
  1. 1.PRISMA Lab, Dipartimento di Informatica e SistemisticaUniversità degli Studi di Napoli Federico IINaplesItaly

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