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Grasping Angle Estimation of Human Forearm with Underactuated Grippers Using Proprioceptive Feedback

  • Francisco PastorEmail author
  • Juan M. Gandarias
  • Alfonso J. García-Cerezo
  • Antonio J. Muñoz-Ramírez
  • Jesús M. Gómez-de-Gabriel
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1093)

Abstract

In this paper, a method for the estimation of the angle of grasping of a human forearm, when grasped by a robot with an underactuated gripper, using proprioceptive information only, is presented. Knowing the angle around the forearm’s axis (i.e. roll angle) is key for the safe manipulation of the human limb and biomedical sensor placement among others. The adaptive gripper has two independent underactuated fingers with two phalanges and a single actuator each. The final joint position of the gripper provides information related to the shape of the grasped object without the need for external contact or force sensors. Regression methods to estimate the roll angle of the grasping have been trained with forearm grasping information from different humans at each angular position. The results show that it is possible to accurately estimate the rolling angle of the human arm, for trained and unknown people.

Keywords

pHRI Underactuated gripper Propioceptive sensors Regression Haptic perception 

Notes

Acknowledgment

This work was supported by the Spanish project DPI2015-65186-R, the European Commission under grant agreement BES-2016-078237, the Telerobotics and Interactive Systems Laboratory (TaIS Lab) and the Systems Engineering and Automation Department, University of Málaga, Spain.

References

  1. 1.
    Armendariz, J., García-Rodríguez, R., Machorro-Fernández, F., Parra-Vega, V.: Manipulation with soft-fingertips for safe pHRI. In: Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 155–156. ACM (2012)Google Scholar
  2. 2.
    Birglen, L.: Enhancing versatility and safety of industrial grippers with adaptive robotic fingers. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2911–2916 (2015)Google Scholar
  3. 3.
    Birglen, L., Gosselin, C.: Optimal design of 2-phalanx underactuated fingers. In: Proceedings of the 2004 International Conference on Intelligent Manipulation and Grasping, pp. 110–116 (2004)Google Scholar
  4. 4.
    Birglen, L., Laliberté, T., Gosselin, C.M.: Underactuated Robotic Hands. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Bowyer, S.A., y Baena, F.R.: Dissipative control for physical human–robot interaction. IEEE Trans. Robot. 31(6), 1281–1293 (2015)Google Scholar
  6. 6.
    Breiman, L.: Classification and Regression Trees. Routledge, Abingdon (2017)CrossRefGoogle Scholar
  7. 7.
    Cao, Z., Hidalgo, G., Simon, T., Wei, S.E., Sheikh, Y.: OpenPose: realtime multi-person 2D pose estimation using part affinity fields. arXiv preprint arXiv:1812.08008 (2018)
  8. 8.
    Chow, K., Kemp, C.C.: Robotic repositioning of human limbs via model predictive control. In: 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 473–480. IEEE (2016)Google Scholar
  9. 9.
    Erickson, Z., Clever, H.M., Turk, G., Liu, C.K., Kemp, C.C.: Deep haptic model predictive control for robot-assisted dressing. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1–8 (2018)Google Scholar
  10. 10.
    Frykberg, E.R.: Medical management of disasters and mass casualties from terrorist bombings: how can we cope? J. Trauma Acute Care Surg. 53(2), 201–212 (2002)CrossRefGoogle Scholar
  11. 11.
    Gandarias, J.M., Gómez-de Gabriel, J.M., García-Cerezo, A.J.: Human and object recognition with a high-resolution tactile sensor. In: IEEE Sensors, pp. 1–3 (2017)Google Scholar
  12. 12.
    Gandarias, J.M., Gómez-de Gabriel, J.M., García-Cerezo, A.J.: Enhancing perception with tactile object recognition in adaptive grippers for human-robot interaction. Sensors 18(3), 692 (2018) CrossRefGoogle Scholar
  13. 13.
    Gandarias, J.M., García-Cerezo, A.J., Gómez-de Gabriel, J.M.: CNN-based methods for object recognition with high-resolution tactile sensors. IEEE Sens. J. 19(16), 6872–6882 (2019).  https://doi.org/10.1109/JSEN.2019.2912968CrossRefGoogle Scholar
  14. 14.
    King, C., Chen, T.L., Jain, A., Kemp, C.C.: Towards an assistive robot that autonomously performs bed baths for patient hygiene. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 319–324 (2010)Google Scholar
  15. 15.
    Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)zbMATHGoogle Scholar
  16. 16.
    Li, Z., Huang, B., Ye, Z., Deng, M., Yang, C.: Physical human-robot interaction of a robotic exoskeleton by admittance control. IEEE Trans. Ind. Electron. 65, 9614–9624 (2018)CrossRefGoogle Scholar
  17. 17.
    Ma, R.R., Odhner, L.U., Dollar, A.M.: A modular, open-source 3D printed underactuated hand. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 2737–2743 (2013)Google Scholar
  18. 18.
    Memar, A.H., Mastronarde, N., Esfahani, E.T.: Design of a novel variable stiffness gripper using permanent magnets. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 2818–2823 (2017)Google Scholar
  19. 19.
    Spiers, A.J., Liarokapis, M.V., Calli, B., Dollar, A.M.: Single-grasp object classification and feature extraction with simple robot hands and tactile sensors. IEEE Trans. Haptics 9(2), 207–220 (2016)CrossRefGoogle Scholar
  20. 20.
    Stilli, A., Cremoni, A., Bianchi, M., Ridolfi, A., Gerii, F., Vannetti, F., Wurdemann, H.A., Allotta, B., Althoefer, K.: AirExGlove - a novel pneumatic exoskeleton glove for adaptive hand rehabilitation in post-stroke patients. In: IEEE International Conference on Soft Robotics (RoboSoft), pp. 579–584 (2018)Google Scholar
  21. 21.
    Williams, C.K.: Prediction with Gaussian processes: from linear regression to linear prediction and beyond. In: Learning in Graphical Models, pp. 599–621. Springer (1998)Google Scholar
  22. 22.
    Yang, C., Zeng, C., Liang, P., Li, Z., Li, R., Su, C.Y.: Interface design of a physical human-robot interaction system for human impedance adaptive skill transfer. IEEE Trans. Autom. Sci. Eng. 15(1), 329–340 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Francisco Pastor
    • 1
    Email author
  • Juan M. Gandarias
    • 1
  • Alfonso J. García-Cerezo
    • 1
  • Antonio J. Muñoz-Ramírez
    • 1
  • Jesús M. Gómez-de-Gabriel
    • 1
  1. 1.Telerobotic and Interactive Systems Laboratory, System Engineering and Automation DepartmentUniversity of MálagaMálagaSpain

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