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Model-Based and Model-Free Robot Control: A Review

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RiTA 2020

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Abstract

Robot control is one of the key aspects of robotics research. Models are essential tools in robotics, such as robot’s own body dynamics and kinematics models, actuator/motor models, and the models of external controllable objects. In this paper, we review the latest advances in model-based and model-free approaches with a strong focus on robot control. Based on the designed search strategy, several prevailing control approaches are classified and discussed according to their control strategies. An insight into the gripper control is also explored. Then the research problems and applicability of the control methods are discussed by investigating their merits and demerits. Based on the discussion, we summarize the challenges and future research trends of robot control.

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References

  1. Pezzato, C., Ferrari, R., Corbato, C.H.: A novel adaptive controller for robot manipulators based on active inference. IEEE Robot. Autom. Lett. 5(2), 2973–2980 (2020). https://doi.org/10.1109/LRA.2020.2974451

    Article  Google Scholar 

  2. Cao, P., Gan, Y., Duan, J., Dai, X.: Passivity-based stable human-robot cooperation with variable admittance control. In: 2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM), pp. 446–451 (2019). https://doi.org/10.1109/ICARM.2019.8833927

  3. Abraham, I., Handa, A., Ratliff, N., Lowrey, K., Murphey, T.D., Fox, D.: Model-based generalization under parameter uncertainty using path integral control. IEEE Robot. Autom. Lett. 5(2), 2864–2871 (2020). https://doi.org/10.1109/LRA.2020.2972836

    Article  Google Scholar 

  4. Sugiarto, I., Conradt, J.: A model-based approach to robot kinematics and control using discrete factor graphs with belief propagation. Rob. Auton. Syst. 91, 234–246 (2017). https://doi.org/10.1016/j.robot.2017.01.003

    Article  Google Scholar 

  5. Williams, G., et al.: Information theoretic MPC for model-based reinforcement learning. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 1714–1721 (2017). https://doi.org/10.1109/ICRA.2017.7989202

  6. Lee, M.A., et al.: Making sense of vision and touch: learning multimodal representations for contact-rich tasks. IEEE Trans. Robot. 36(3), 582–596 (2020). https://doi.org/10.1109/TRO.2019.2959445

    Article  Google Scholar 

  7. Fang, G., et al.: Vision-based online learning kinematic control for soft robots using local gaussian process regression. IEEE Robot. Autom. Lett. 4(2), 1194–1201 (2019). https://doi.org/10.1109/LRA.2019.2893691

    Article  Google Scholar 

  8. Chen, X., Wang, N., Cheng, H., Yang, C.: Neural learning enhanced variable admittance control for human-robot collaboration. IEEE Access 8, 25727–25737 (2020). https://doi.org/10.1109/ACCESS.2020.2969085

    Article  Google Scholar 

  9. Kang, G., Oh, H.S., Seo, J.K., Kim, U., Choi, H.R.: Variable admittance control of robot manipulators based on human intention. IEEE/ASME Trans. Mechatronics 24(3), 1023–1032 (2019). https://doi.org/10.1109/TMECH.2019.2910237

    Article  Google Scholar 

  10. Katzschmann, R.K., Santina, C.D., Toshimitsu, Y., Bicchi, A., Rus, D.: Dynamic motion control of multi-segment soft robots using piecewise constant curvature matched with an augmented rigid body model. In: 2019 2nd IEEE International Conference on Soft Robotics (RoboSoft), pp. 454–461 (2019). https://doi.org/10.1109/ROBOSOFT.2019.8722799

  11. Xu, Z., Li, S., Zhou, X., Cheng, T.: Dynamic neural networks based adaptive admittance control for redundant manipulators with model uncertainties. Neurocomputing 357, 271–281 (2019). https://doi.org/10.1016/j.neucom.2019.04.069

    Article  Google Scholar 

  12. Peng, J., Yang, Z., Ma, T.: Position/force tracking impedance control for robotic systems with uncertainties based on adaptive Jacobian and neural network. Complexity 2019, 1406534 (2019). https://doi.org/10.1155/2019/1406534

    Article  MATH  Google Scholar 

  13. Wang, R., Goyal, R., Chakravorty, S., Skelton, R.E.: Model and data based approaches to the control of tensegrity robots. IEEE Robot. Autom. Lett. 5(3), 3846–3853 (2020)

    Article  Google Scholar 

  14. Van Toan, N., Khoi, P.B.: Fuzzy-based-admittance controller for safe natural human–robot interaction. Adv. Robot. 33(15–16), 815–823 (2019). https://doi.org/10.1080/01691864.2019.1607551

    Article  Google Scholar 

  15. Ferraguti, F., Talignani Landi, C., Sabattini, L., Bonfè, M., Fantuzzi, C., Secchi, C.: A variable admittance control strategy for stable physical human–robot interaction. Int. J. Rob. Res. 38(6), 747–765 (2019). https://doi.org/10.1177/0278364919840415

    Article  Google Scholar 

  16. Wang, H., Wang, Z., Wang, H.: Impedance control strategy and experimental analysis of collaborative robots based on torque feedback. In: 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 2951–2957 (2019). https://doi.org/10.1109/ROBIO49542.2019.8961470

  17. Hu, H., Wang, X., Chen, L.: Impedance sliding mode control with adaptive fuzzy compensation for robot-environment interacting. IEEE Access 8, 19880–19889 (2020). https://doi.org/10.1109/ACCESS.2020.2968954

    Article  Google Scholar 

  18. Wu, M., Taetz, B., Saraiva, E.D., Bleser, G., Liu, S.: On-line motion prediction and adaptive control in human-robot handover tasks. In: 2019 IEEE International Conference on Advanced Robotics and its Social Impacts (ARSO), pp. 1–6 (2019)

    Google Scholar 

  19. Qiao, Z., Nguyen, P.H., Polygerinos, P., Zhang, W.: Dynamic modeling and motion control of a soft robotic arm segment. In: 2019 American Control Conference (ACC), pp. 5438–5443 (2019)

    Google Scholar 

  20. Bhandari, B., Lee, M.: Haptic identification of objects using tactile sensing and computer vision. Adv. Mech. Eng. 11(4), 1687814019840468 (2019). https://doi.org/10.1177/1687814019840468

    Article  Google Scholar 

  21. Falco, P., Lu, S., Natale, C., Pirozzi, S., Lee, D.: A transfer learning approach to cross-modal object recognition: from visual observation to robotic haptic exploration. IEEE Trans. Robot. 35(4), 987–998 (2019)

    Article  Google Scholar 

  22. Liu, P., Yu, H., Cang, S.: Modelling and analysis of dynamic frictional interactions of vibro-driven capsule systems with viscoelastic property. Eur. J. Mech. - A/Solids 74, 16–25 (2019). https://doi.org/10.1016/j.euromechsol.2018.10.016

    Article  MathSciNet  MATH  Google Scholar 

  23. Liu, P., Neumann, G., Fu, Q., Pearson, S., Yu, H.: Energy-efficient design and control of a vibro-driven robot. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1464–1469 (2018). https://doi.org/10.1109/IROS.2018.8594322

  24. Liu, P., Huda, M., Tang, Z., Sun, L.: A self-propelled robotic system with a visco-elastic joint: dynamics and motion analysis. Eng. Comput. 36(2), 655–669 (2019). https://doi.org/10.1007/s00366-019-00722-3

    Article  Google Scholar 

  25. Huda, M., Liu, P., Saha, C., Yu, H.: Modelling and motion analysis of a pill-sized hybrid capsule robot. J. Intell. Robot. Syst. 100(3–4), 753–764 (2020). https://doi.org/10.1007/s10846-020-01167-3

    Article  Google Scholar 

  26. Liu, P., Yu, H., Cang, S.: Optimized adaptive tracking control for an underactuated vibro-driven capsule system. Nonlinear Dyn. 94(3), 1803–1817 (2018). https://doi.org/10.1007/s11071-018-4458-9

    Article  Google Scholar 

  27. Esmaeili, B., Salim, M., Baradarannia, M., Farzamnia, A.: Data-driven observer-based model-free adaptive discrete-time terminal sliding mode control of rigid robot manipulators. In: 2019 7th International Conference on Robotics and Mechatronics (ICRoM), pp. 432–438 (2019). https://doi.org/10.1109/ICRoM48714.2019.9071819

  28. Cremer, S., Das, S.K., Wijayasinghe, I.B., Popa, D.O., Lewis, F.L.: Model-free online neuroadaptive controller with intent estimation for physical human-robot interaction. IEEE Trans. Robot. 36(1), 240–253 (2020). https://doi.org/10.1109/TRO.2019.2946721

    Article  Google Scholar 

  29. Saleki, A., Fateh, M.M.: Adaptive model-free control of electrically driven robot manipulators. In: 2019 27th Iranian Conference on Electrical Engineering (ICEE), pp. 1086–1091 (2019)

    Google Scholar 

  30. Perrusquía, A., Yu, W., Soria, A.: Optimal contact force of robots in unknown environments using reinforcement learning and model-free controllers. In: 2019 16th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), pp. 1–6 (2019). https://doi.org/10.1109/ICEEE.2019.8884518

  31. Wu, W., Li, D., Meng, W., Zuo, J., Liu, Q., Ai, Q.: Iterative feedback tuning-based model-free adaptive iterative learning control of pneumatic artificial muscle. In: 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 954–959 (2019). https://doi.org/10.1109/AIM.2019.8868584

  32. Reinhart, R., Shareef, Z., Steil, J.: Hybrid analytical and data-driven modeling for feed-forward robot control †. Sensors 17(2), 311 (2017). https://doi.org/10.3390/s17020311

    Article  Google Scholar 

  33. Carron, A., Arcari, E., Wermelinger, M., Hewing, L., Hutter, M., Zeilinger, M.N.: Data-driven model predictive control for trajectory tracking with a robotic arm. IEEE Robot. Autom. Lett. 4(4), 3758–3765 (2019). https://doi.org/10.1109/LRA.2019.2929987

    Article  Google Scholar 

  34. Liu, P., Yu, H., Cang, S.: Adaptive neural network tracking control for underactuated systems with matched and mismatched disturbances. Nonlinear Dyn. 98(2), 1447–1464 (2019). https://doi.org/10.1007/s11071-019-05170-8

    Article  Google Scholar 

  35. Shyam, R.B., Lightbody, P., Das, G., Liu, P., Gomez-Gonzalez, S., Neumann, G.: Improving local trajectory optimisation using probabilistic movement primitives (2019)

    Google Scholar 

  36. Tang, Z., Yu, H., Lu, C., Liu, P., Jin, X.: Single-trial classification of different movements on one arm based on ERD/ERS and corticomuscular coherence. IEEE Access 7, 128185–128197 (2019). https://doi.org/10.1109/ACCESS.2019.2940034

    Article  Google Scholar 

  37. Sun, L., Zhao, C., Yan, Z., Liu, P., Duckett, T., Stolkin, R.: A novel weakly-supervised approach for RGB-D-based nuclear waste object detection. IEEE Sens. J. 19(9), 3487–3500 (2019). https://doi.org/10.1109/JSEN.2018.2888815

    Article  Google Scholar 

  38. Deng, Z., Jonetzko, Y., Zhang, L., Zhang, J.: Grasping force control of multi-fingered robotic hands through tactile sensing for object stabilization. Sensors 20(4) (2020). https://doi.org/10.3390/s20041050

  39. Guo, M., Wu, P., Yi, B., Gealy, D., McKinley, S., Abbeel, P.: Blue gripper: a robust, low-cost, and force-controlled robot hand. In: 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), pp. 1505–1510 (2019). https://doi.org/10.1109/COASE.2019.8843134

  40. Li, Y., Chen, Y., Li, Y.: Pre-charged pneumatic soft gripper with closed-loop control. IEEE Robot. Autom. Lett. 4(2), 1402–1408 (2019). https://doi.org/10.1109/LRA.2019.2895877

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of York, York, YO10 5GH, UK

    Bowei Zhang & Pengcheng Liu

Authors
  1. Bowei Zhang
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  2. Pengcheng Liu
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Corresponding author

Correspondence to Pengcheng Liu .

Editor information

Editors and Affiliations

  1. Cardiff Metropolitan University, Cardiff, UK

    Esyin Chew

  2. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia

    Anwar P. P. Abdul Majeed

  3. University of York, York, UK

    Pengcheng Liu

  4. Cardiff Metropolitan University, Cardiff, UK

    Jon Platts

  5. School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Hyun Myung

  6. School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Junmo Kim

  7. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Jong-Hwan Kim

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Zhang, B., Liu, P. (2021). Model-Based and Model-Free Robot Control: A Review. In: Chew, E., et al. RiTA 2020. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-4803-8_6

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