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Proprioceptive Sensor-Based Simultaneous Multi-Contact Point Localization and Force Identification for Robotic Arms (2303.03903v1)

Published 7 Mar 2023 in cs.RO

Abstract: In this paper, we propose an algorithm that estimates contact point and force simultaneously. We consider a collaborative robot equipped with proprioceptive sensors, in particular, joint torque sensors (JTSs) and a base force/torque (F/T) sensor. The proposed method has the following advantages. First, fast computation is achieved by proper preprocessing of robot meshes. Second, multi-contact can be identified with the aid of the base F/T sensor, while this is challenging when the robot is equipped with only JTSs. The proposed method is a modification of the standard particle filter to cope with mesh preprocessing and with available sensor data. In simulation validation, for a 7 degree-of-freedom robot, the algorithm runs at 2200Hz with 99.96% success rate for the single-contact case. In terms of the run-time, the proposed method was >=3.5X faster compared to the existing methods. Dual and triple contacts are also reported in the manuscript.

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References (29)
  1. D. M. Ebert and D. D. Henrich, “Safe human-robot-cooperation: Image-based collision detection for industrial robots,” in IEEE/RSJ international conference on intelligent robots and systems, vol. 2, 2002, pp. 1826–1831.
  2. S. Kuhn and D. Henrich, “Fast vision-based minimum distance determination between known and unkown objects,” in 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2186–2191.
  3. K. Kim and M. J. Kim, “A reachability tree-based algorithm for robot task and motion planning,” in 2023 IEEE International Conference on Robotics and Automation (ICRA).
  4. M. J. Kim, W. Lee, J. Y. Choi, G. Chung, K.-L. Han, I. S. Choi, C. Ott, and W. K. Chung, “A passivity-based nonlinear admittance control with application to powered upper-limb control under unknown environmental interactions,” IEEE/ASME Transactions on Mechatronics, vol. 24, no. 4, pp. 1473–1484, 2019.
  5. M. J. Kim, W. Lee, C. Ott, and W. K. Chung, “A passivity-based admittance control design using feedback interconnections,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 801–807.
  6. A. De Luca, A. Albu-Schaffer, S. Haddadin, and G. Hirzinger, “Collision detection and safe reaction with the dlr-iii lightweight manipulator arm,” in 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1623–1630.
  7. M. J. Kim, Y. J. Park, and W. K. Chung, “Design of a momentum-based disturbance observer for rigid and flexible joint robots,” Intelligent Service Robotics, vol. 8, pp. 57–65, 2015.
  8. Y. J. Heo, D. Kim, W. Lee, H. Kim, J. Park, and W. K. Chung, “Collision detection for industrial collaborative robots: A deep learning approach,” IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 740–746, 2019.
  9. S. A. B. Birjandi, J. Kühn, and S. Haddadin, “Observer-extended direct method for collision monitoring in robot manipulators using proprioception and imu sensing,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 954–961, 2020.
  10. D. Kim, D. Lim, and J. Park, “Transferable collision detection learning for collaborative manipulator using versatile modularized neural network,” IEEE Transactions on Robotics, 2021.
  11. K. M. Park, Y. Park, S. Yoon, and F. C. Park, “Collision detection for robot manipulators using unsupervised anomaly detection algorithms,” IEEE/ASME Transactions on Mechatronics, 2021.
  12. J. M. Gandarias, A. J. García-Cerezo, and J. M. Gómez-de Gabriel, “Cnn-based methods for object recognition with high-resolution tactile sensors,” IEEE Sensors Journal, vol. 19, no. 16, pp. 6872–6882, 2019.
  13. J. Liang, J. Wu, H. Huang, W. Xu, B. Li, and F. Xi, “Soft sensitive skin for safety control of a nursing robot using proximity and tactile sensors,” IEEE Sensors Journal, vol. 20, no. 7, pp. 3822–3830, 2020.
  14. A. Zwiener, C. Geckeler, and A. Zell, “Contact point localization for articulated manipulators with proprioceptive sensors and machine learning,” in 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 323–329.
  15. S. Haddadin, A. De Luca, and A. Albu-Schäffer, “Robot collisions: A survey on detection, isolation, and identification,” IEEE Transactions on Robotics, vol. 33, no. 6, pp. 1292–1312, 2017.
  16. G. Buondonno and A. De Luca, “Combining real and virtual sensors for measuring interaction forces and moments acting on a robot,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 794–800.
  17. M. Iskandar, O. Eiberger, A. Albu-Schäffer, A. De Luca, and A. Dietrich, “Collision detection, identification, and localization on the dlr sara robot with sensing redundancy,” in 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 3111–3117.
  18. T. Pang, J. Umenberger, and R. Tedrake, “Identifying external contacts from joint torque measurements on serial robotic arms and its limitations,” in 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 6476–6482.
  19. D. Popov, A. Klimchik, and A. Pashkevich, “Real-time estimation of multiple potential contact locations and forces,” IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7025–7032, 2021.
  20. L. Manuelli and R. Tedrake, “Localizing external contact using proprioceptive sensors: The contact particle filter,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5062–5069.
  21. A. Zwiener, R. Hanten, C. Schulz, and A. Zell, “Armcl: Arm contact point localization via monte carlo localization,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7105–7111.
  22. J. Bimbo, C. Pacchierotti, N. G. Tsagarakis, and D. Prattichizzo, “Collision detection and isolation on a robot using joint torque sensing,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7604–7609.
  23. D. Popov and A. Klimchik, “Real-time external contact force estimation and localization for collaborative robot,” in 2019 IEEE International Conference on Mechatronics (ICM), vol. 1, pp. 646–651.
  24. N. Likar and L. Žlajpah, “External joint torque-based estimation of contact information,” International Journal of Advanced Robotic Systems, vol. 11, no. 7, p. 107, 2014.
  25. S. Haddadin, A. Albu-Schaffer, A. De Luca, and G. Hirzinger, “Collision detection and reaction: A contribution to safe physical human-robot interaction,” in 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3356–3363.
  26. D.-M. Yan, B. Lévy, Y. Liu, F. Sun, and W. Wang, “Isotropic remeshing with fast and exact computation of restricted voronoi diagram,” in Computer graphics forum, vol. 28, no. 5.   Wiley Online Library, 2009, pp. 1445–1454.
  27. A. Jacobson et al., “gptoolbox: Geometry processing toolbox,” 2021, http://github.com/alecjacobson/gptoolbox.
  28. E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5026–5033.
  29. A. G. Pandala, Y. Ding, and H.-W. Park, “qpswift: A real-time sparse quadratic program solver for robotic applications,” IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 3355–3362, 2019.
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