Discrete-Time Formulation for Optimal Impact Control in Interaction Tasks

  • Loris Roveda
  • Niccoló Iannacci
  • Lorenzo Molinari Tosatti
Article
  • 45 Downloads

Abstract

Lightweight manipulators are increasingly involved in industrial scenarios due to their intrinsic safety features allowing to share working space and to cooperate with humans/robots. In particular, interaction tasks are one of their main application. In fact, by imposing a compliant behavior (at software or hardware level) a target interaction can be tracked while ensuring safety during the whole task. Despite the wide range of control strategies developed to face the interaction control problem, the limited control frequency and the measurements noise (especially considering the estimation of end-effector wrenchs from joint side measurements/estimation) are the main limitation in order to achieve improved interaction tracking performance. This paper presents a discrete time formulation for impedance controlled tasks granting a free-overshoot contact force throughout the whole contact phase between the robot and a partially unknown environment, involving finite sampling and force measurements filtering. Moreover, since many applications require the manipulator to approach the not well-known positioned target environment, the proposed algorithm is capable to avoid any force overshoot during the initial contact phase, taking into account non-zero approaching velocities. The main control structure is used in both the free-motion and contact phases, without switching from different control laws, by properly optimizing the control gains solving the defined LQR optimal control problem. A probing task has been carried out in order to validate the control performance with particular attention to the smoothness of the response. Results show the avoidance of force overshoots and instabilities. Moreover, the method has been compared to a continuous time control algorithm, showing improved performance.

Keywords

Force overshoots avoidance Impedance control Set-point deformation Discrete control frequency Force filtering Interaction tasks Unknown environment estimation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

The work has been developed within the project Four-By-Three, funded from HORIZON-2020 research and innovation programme under grant agreement n. 637095.

References

  1. 1.
    Peternel, L., Petriċ, T., Babiċ, J.: Human-in-the-loop approach for teaching robot assembly tasks using impedance control interface. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 1497–1502. IEEE, New York (2015)Google Scholar
  2. 2.
    Rozo, L., Calinon, S., Caldwell, D.G., Jimenez, P., Torras, C.: Learning physical collaborative robot behaviors from human demonstrations. IEEE Trans. Robot 32(3), 513–527 (2016).  https://doi.org/10.1109/TRO.2016.2540623 . ISSN 1552-3098 http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7450630 CrossRefGoogle Scholar
  3. 3.
    de Silva, C.W.: Some issues and applications of multi-robot cooperation. In: 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 2–2. IEEE, New York (2016)Google Scholar
  4. 4.
    Universal Robot: Universal robot ur10 (visited january 2017). http://www.universal-robots.com/ (2012)
  5. 5.
    Vicentini, F., Giussani, M., Tosatti, L.M.: Trajectory-dependent safe distances in human-robot interaction. In: Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA), pp. 1–4. IEEE, New York (2014)Google Scholar
  6. 6.
    van der Vorm, J., Nugent, R., O’Sullivan, L.: Safety and risk management in designing for the lifecycle of an exoskeleton: a novel process developed in the robo-mate project. Procedia Manuf 3, 1410–1417 (2015)CrossRefGoogle Scholar
  7. 7.
    Rethink: Baxter research robot (visited january 2017). http://www.rethinkrobotics.com/baxter-research-robot/ (2012)
  8. 8.
    Albu-Schäffer, A., Ott, C., Hirzinger, G.: A unified passivity-based control framework for position, torque and impedance control of flexible joint robots. Int. J. Robot. Res. 26(1), 23–39 (2007).  https://doi.org/10.1177/0278364907073776 CrossRefMATHGoogle Scholar
  9. 9.
    Doggett, W.: Robotic assembly of truss structures for space systems and future research plans. In: Aerospace Conference Proceedings, 2002. IEEE, vol. 7, pp. 7–3589. IEEE, New York (2002)Google Scholar
  10. 10.
    Márquez, J.J., Pérez, J.M., Rıos, J., Vizán, A.: Process modeling for robotic polishing. J. Mater. Process. Technol. 159(1), 69–82 (2005)CrossRefGoogle Scholar
  11. 11.
    Rembala, R., Ower, C.: Robotic assembly and maintenance of future space stations based on the iss mission operations experience. Acta Astronaut 65(7), 912–920 (2009)CrossRefGoogle Scholar
  12. 12.
    Tsai, M.-J., Huang, J.F., Kao, W.L.: Robotic polishing of precision molds with uniform material removal control. Int. J. Mach. Tools Manuf. 49(11), 885–895 (2009)CrossRefGoogle Scholar
  13. 13.
    Pirrotta, S.: Preliminary study on a novel coring system for planetary surface sampling. In: Proceedings of the 7th International Planetary Probe Workshop, pp. 14–18. Barcelona (2010)Google Scholar
  14. 14.
    Stolt, A., Linderoth, M., Robertsson, A., Johansson, R.: Robotic assembly of emergency stop buttons. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2081–2081. IEEE, New York (2013)Google Scholar
  15. 15.
    Li, J.Z., Rui Qin, H., Yi, W.M., Hao, F., Tang, L.Y.: A study of flexible force control method on robotic assembly for spacecraft. In: Applied Mechanics and Materials, vol. 681, pp. 79–85. Trans Tech Publ, Zürich (2014)Google Scholar
  16. 16.
    Salisbury, J.K.: Active stiffness control of a manipulator in cartesian coordinates. In: 1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes, vol. 19, pp. 95–100.  https://doi.org/10.1109/CDC.1980.272026 (1980)
  17. 17.
    Mason, M.T.: Compliance and force control for computer controlled manipulators. IEEE Trans. Syst. Man. Cybern. 11(6), 418– 432 (1981)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Raibert, M.H., Craig, J.J.: Hybrid position/force control of manipulators. J. Dyn. Syst. Meas. Control 103(2), 126–133 (1981).  https://doi.org/10.1115/1.3139652 CrossRefGoogle Scholar
  19. 19.
    Hogan, N.: Impedance control: An approach to manipulation. In: American Control Conference 1984, pp. 304–313 (1984)Google Scholar
  20. 20.
    Colgate, E., Hogan, N.: An analysis of contact instability in terms of passive physical equivalents. In: 1989 IEEE International Conference on Robotics and Automation (ICRA), pp. 404–409. IEEE, New York (1989),  https://doi.org/10.1109/ROBOT.1989.100021
  21. 21.
    Lange, F., Jehle, C., Suppa, M., Hirzinger, G.: Revised force control using a compliant sensor with a position controlled robot. In: 2012 IEEE International Conference on Robotics and Automation (ICRA), pp. 1532–1537. IEEE, New York (2012)Google Scholar
  22. 22.
    Lange, F., Bertleff, W., Suppa, M.: Force and trajectory control of industrial robots in stiff contact. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 2927–2934. IEEE, New York (2013)Google Scholar
  23. 23.
    Ott, C., Mukherjee, R., Nakamura, Y.: Unified impedance and admittance control. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 554–561. IEEE, New York (2010)Google Scholar
  24. 24.
    Roveda, L., Vicentini, F., Tosatti, L.M.: Deformation-tracking impedance control in interaction with uncertain environments. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1992–1997. IEEE, New York (2013)Google Scholar
  25. 25.
    Ott, C., Albu-Schaffer, A., Hirzinger, G.: A cartesian compliance controller for a manipulator mounted on a flexible structure. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4502–4508. IEEE, New York (2006)Google Scholar
  26. 26.
    Roveda, L., Pedrocchi, N., Vicentini, F., Tosatti, L.M.: Industrial compliant robot bases in interaction tasks: a force tracking algorithm with coupled dynamics compensation. Robotica, 1–15Google Scholar
  27. 27.
    Roveda, L., Pedrocchi, N., Vicentini, F., Tosatti, L.M.: An interaction controller formulation to systematically avoid force overshoots through impedance shaping method with compliant robot base. Mechatronics 39, 42–53 (2016)CrossRefGoogle Scholar
  28. 28.
    Ferraguti, F., Secchi, C., Fantuzzi, C.: A tank-based approach to impedance control with variable stiffness. In: Proceedings of the 2013 International Conference on Robotics and Automation (ICRA) (2013)Google Scholar
  29. 29.
    Schindlbeck, C., Haddadin, S.: Unified passivity-based cartesian force/impedance control for rigid and flexible joint robots via task-energy tanks. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 440–447. IEEE, New York (2015)Google Scholar
  30. 30.
    Roveda, L., Pedrocchi, N., Tosatti, L.M.: Exploiting impedance shaping approaches to overcome force overshoots in delicate interaction tasks. Int. J. Adv. Robot Syst.  https://doi.org/10.1177/1729881416662771 (2016)
  31. 31.
    Calanca, A., Muradore, R., Fiorini, P.: A review of algorithms for compliant control of stiff and fixed-compliance robots. IEEE/ASME Trans. Mechatron. 21(2), 613–624 (2016)CrossRefGoogle Scholar
  32. 32.
    Villani, L., Canudas de Wit, C., Brogliato, B.: An exponentially stable adaptive control for force and position tracking of robot manipulators. IEEE Trans. Autom. Control 44(4), 798–802 (1999).  https://doi.org/10.1109/9.754821. ISSN 0018-9286
  33. 33.
    Seraji, H., Colbaugh, R.: Adaptive force-based impedance control. In: Proceedings of the 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems’ 93, IROS’93, vol. 3, pp. 1537–1544. IEEE, New York (1993)Google Scholar
  34. 34.
    Seraji, H., Colbaugh, R.: Force tracking in impedance control. Int. J. Robot. Res. 16(1), 97–117 (1997)CrossRefGoogle Scholar
  35. 35.
    Jung, S., Hsia, T.C., Bonitz, R.G.: Force tracking impedance control of robot manipulators under unknown environment. IEEE Trans. Control Syst. Technol. 12(3), 474–483 (2004).  https://doi.org/10.1109/TCST.2004.824320. ISSN 1063-6536
  36. 36.
    Mohammadi, H., Richter, H.: Robust tracking/impedance control: Application to prosthetics. In: American Control Conference (ACC) 2015, pp. 2673–2678. IEEE, New York (2015)Google Scholar
  37. 37.
    Ikeura, R., Inooka, H.: Variable impedance control of a robot for cooperation with a human. In: 1995 IEEE International Conference on Robotics and Automation (ICRA), vol. 3, pp. 3097–3102. IEEE, New York (1995)Google Scholar
  38. 38.
    Lee, K., Buss, M.: Force tracking impedance control with variable target stiffness. Intern. Federation Autom. Control 16(1), 6751–6756 (2000)Google Scholar
  39. 39.
    Yang, C., Ganesh, G., Haddadin, S., Parusel, S., Albu-Schaeffer, A., Burdet, E.: Human-like adaptation of force and impedance in stable and unstable interactions. IEEE Trans. Robot. 27(5), 918–930 (2011)CrossRefGoogle Scholar
  40. 40.
    Sehoon, O., Woo, H., Kong, K.: Frequency-shaped impedance control for safe human–robot interaction in reference tracking application (2014)Google Scholar
  41. 41.
    Sehoon, O., Woo, H., Kong, K.: Stability and robustness analysis of frequency-shaped impedance control for reference tracking and compliant interaction. In: World Congress, vol. 19, pp. 3557–3562 (2014)Google Scholar
  42. 42.
    Motoi, N., Shimono, T., Kubo, R., Kawamura, A.: Task realization by a force-based variable compliance controller for flexible motion control systems. IEEE Trans. Ind. Electron. 61(2), 1009–1021 (2014)CrossRefGoogle Scholar
  43. 43.
    Wang, W.-C., Lee, C.-H.: Fuzzy neural network-based adaptive impedance force control design of robot manipulator under unknown environment. In: 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1442–1448. IEEE, New York (2014)Google Scholar
  44. 44.
    Kim, T., Kim, H.S., Kim, J.: Position-based impedance control for force tracking of a wall-cleaning unit. Int. J. Precis. Eng. Manuf. 17(3), 323–329 (2016)CrossRefGoogle Scholar
  45. 45.
    Roveda, L., Vicentini, F., Pedrocchi, N., Tosatti, L.M.: Force-tracking impedance control for manipulators mounted on compliant bases. In: 2014 IEEE International Conference on Robotics and Automation (ICRA). IEEE, New York (2014)Google Scholar
  46. 46.
    Roveda, L., Pedrocchi, V.F., Tosatti, L.M.: Impedance control based force-tracking algorithm for interaction robotics tasks: An analytically force overshoots-free approach. In: Informatics in Control, Automation and Robotics, 2015. IEEE, New York (2015)Google Scholar
  47. 47.
    Roveda, L., Iannacci, N., Vicentini, F., Pedrocchi, N., Braghin, F., Tosatti, L.M.: Optimal impedance force-tracking control design with impact formulation for interaction tasks. IEEE Robot. Autom. Lett. 1(1), 130–136 (2016)CrossRefGoogle Scholar
  48. 48.
    Roveda, L., Vicentini, F., Pedrocchi, N., Braghin, F., Tosatti, L.M.: Impedance shaping controller for robotic applications in interaction with compliant environments. In: 2014 11th International Conference on Informatics in Control, Automation and Robotics, vol. 2, pp. 444–450. IEEE, New York (2014)Google Scholar
  49. 49.
    Anwar, G., Tomizuka, M., Horowitz, R., Kubo, T.: Experimental study on discrete time adaptive control of an industrial robot arm. In: Adaptive Systems in Control and Signal Processing 1986: Proceedings of the 2nd IFAC Workshop, Lund, Sweden, 1-3 July 1986, p. 265. Elsevier, New York (2014)Google Scholar
  50. 50.
    Morita, Y., Okada, H., Ukai, H., Kando, H., Matsui, N.: Optimal force control of elastic robot with contact motion to environment. In: AMC’98-Coimbra., 1998 5th Int Workshop on Advanced Motion Control, 1998, pp. 228–233. IEEE, New York (1998)Google Scholar
  51. 51.
    Huang, P., Wang, D., Meng, Z., Zhang, F., Liu, Z.: Impact dynamic modeling and adaptive target capturing control for tethered space robots with uncertainties. IEEE/ASME Trans. Mechatronics 21(5), 2260–2271 (2016)CrossRefGoogle Scholar
  52. 52.
    Flügge, W.: Viscoelasticity. Springer, New York (1975)CrossRefMATHGoogle Scholar
  53. 53.
    Erickson, D., Weber, M., Sharf, I.: Contact stiffness and damping estimation for robotic systems. Int. J. Robot. Res. 22(1), 41–57 (2003)CrossRefGoogle Scholar
  54. 54.
    Kalman, R.E., et al.: Contributions to the theory of optimal control. Bol. Soc. Mat. Mexicana 5(2), 102–119 (1960)MathSciNetMATHGoogle Scholar
  55. 55.
    Roveda, L.: Model Based Compliance Shaping Control of Light-Weight Manipulator in Hard-Contact Industrial Applications. PhD thesis. Politecnico di Milano, Mechanical Engineering Department, Italy (2015)Google Scholar
  56. 56.
    Farina, L., Rinaldi, S.: Positive Linear Systems: Theory and Applications, vol. 50. Wiley, New York (2011)Google Scholar
  57. 57.
    Chung, W.J., Kim, I.H., Joh, J.: Null-space dynamics-based control of redundant manipulators in reducing impact. Control. Eng. Pract. 5(9), 1273–1282 (1997)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Loris Roveda
    • 1
  • Niccoló Iannacci
    • 1
  • Lorenzo Molinari Tosatti
    • 1
  1. 1.Institute of Industrial Technologies and Automation (ITIA) of Italian National Research Council (CNR)MilanItaly

Personalised recommendations