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Advancing Closed-Chain Robot Control Through Model Predictive Techniques and Virtual Breakpoints

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Social Robotics (ICSR + BioMed 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14916))

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Abstract

This article introduces a model predictive control method for closed-chain robots. By utilizing virtual breakpoints, this method simplifies complex closed-chain structures into more easily controllable open chains, facilitating standard kinematic analysis and the application of Model Predictive Control (MPC). The method incorporates the Iterative Linear Quadratic Regulator (iLQR), an effective Hessian approximation variant of Differential Dynamic Programming (DDP), known for its ability to handle discrete-time optimal control challenges efficiently. This technique integrates geometric constraints with dynamic simulation through the OCS2 framework to implement ILQR-MPC, ensuring precise motion prediction and enhancing real-time performance, which is crucial for dynamic systems. Initial theoretical and simulation validations on the Gazebo platform show promising results, with plans for further research to refine and validate this methodology. This innovative approach provides a robust and adaptable solution for controlling closed-chain mechanisms, significantly improving their design and operational capabilities.

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References

  1. Gallardo-Alvarado, J.: A Gough–Stewart parallel manipulator with configurable platform and multiple end-effectors. Meccanica 03, 597–613 (2020)

    Article  MathSciNet  Google Scholar 

  2. Zhao, G.H., Wang, D., Liu, L.B., Guo, J.Z.: Positioning error compensation for parallel mechanism with two kinematic calibration methods. Chin. J. Aeronaut. 09, 2472–2489 (2020)

    Article  Google Scholar 

  3. Lu, F., Zhang, Y., Wang, K.: Design and kinematics analysis of a novel uncoupled two-translational parallel robot mechanism. In 2021 3rd International Conference on Robotics and Computer Vision (ICRCV), Beijing, China: IEEE, pp. 30–34 (2021). https://doi.org/10.1109/ICRCV52986.2021.9546973

  4. Berger, J., Unger, M., Keller, J., Reich, C.M., Neumuth, T., Melzer, A.: Design and validation of a medical robotic device system to control two collaborative robots for ultrasound-guided needle insertions. Front. Robot. AI 9, 875845 (2022). https://doi.org/10.3389/frobt.2022.875845

    Article  Google Scholar 

  5. Xie, S., Hu, K., Liu, H., Wan, Y.: Dynamic modeling and performance analysis of a new redundant parallel rehabilitation robot. IEEE Access 8, 222211–222225 (2020). https://doi.org/10.1109/ACCESS.2020.3043429

    Article  Google Scholar 

  6. Hu, A., Yin, Q., He, C., Dun, X., Ai, J.: Research on 4-UPUS Parallel Mechanism Ankle Joint Rehabilitation Training Robot. (2022). https://doi.org/10.20944/preprints202202.0179.v2

  7. Yan, H., et al.: Configuration design of an upper limb rehabilitation robot with a generalized shoulder joint. Appl. Sci. 11(5), 2080 (2021). https://doi.org/10.3390/app11052080

    Article  Google Scholar 

  8. Wang, Q., Min, H., Guo, Y.: An algorithm for trajectory optimization of dual-arm coordination based on arm angle constraints. Cobot 1, 10 (2022). https://doi.org/10.12688/cobot.17470.1

    Article  Google Scholar 

  9. Sovukluk, S., Englsberger, J., Ott.: Whole body control formulation for humanoid robots with closed/parallel kinematic chains: kangaroo case study. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 10390–10396. Detroit, MI, USA, IEEE (2023). https://doi.org/10.1109/IROS55552.2023.10341391

  10. Xu, R., Luo, J., Wang, M.: Kinematic and dynamic manipulability analysis for free-floating space robots with closed chain constraints. Robot. Auton. Syst. 130, 103548 (2020). https://doi.org/10.1016/j.robot.2020.103548

    Article  Google Scholar 

  11. Lv, X., Cheng, L., Chen, X.: Kinematic control of multiple manipulators collaborative handling system. In 2023 4th International Conference on Big Data and Artificial Intelligence and Software Engineering (ICBASE), pp. 469–473. Nanjing, China, IEEE (2023). https://doi.org/10.1109/ICBASE59196.2023.10303217

  12. Featherstone, R.: Rigid body Dynamics Algorithms. Springer, New York (2008)

    Book  Google Scholar 

  13. Yang, S., et al.: Improved task space locomotion controller for a quadruped robot with parallel mechanisms. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 13578–13585. Kyoto, Japan, IEEE (2022). https://doi.org/10.1109/IROS47612.2022.9981385

  14. Kleff, S., Meduri, A., Budhiraja, R., Mansard, N., Righetti, L.: High-frequency nonlinear model predictive control of a manipulator. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 7330–7336. Xi’an, China, IEEE (2021),. https://doi.org/10.1109/ICRA48506.2021.9560990

  15. Sideris, A., Bobrow, J.E.: An efficient sequential linear quadratic algorithm for solving nonlinear optimal control problems. IEEE Trans. Autom. Control 50(12), 2043–2047 (2005). https://doi.org/10.1109/TAC.2005.860248

    Article  MathSciNet  Google Scholar 

  16. Howell, T.A., Jackson, B.E., Manchester, Z.: ALTRO: A Fast Solver for Constrained Trajectory Optimization. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7674–7679. Macau, China, IEEE (2019). https://doi.org/10.1109/IROS40897.2019.8967788

  17. Farshidian, F., Neunert, M., Winkler, A.W, Rey, G., Buchli, J.: An efficient optimal planning and control framework for quadrupedal locomotion. In 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, pp. 93–100. Singapore, IEEE (2017). https://doi.org/10.1109/ICRA.2017.7989016

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Acknowledgments

This study was funded by Shaanxi Province key industrial innovation chain (group)- industrial field project (2018ZDCXL-GY-06–08) and National Natural Science Foundation of China (Grant No. 51375390).

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Authors and Affiliations

  1. Northwestern Polytechnical University, Xi’an, 710129, China

    Jiayang Zhang, Huashan Feng, Yebiao Zhang & Lefan Kang

Authors
  1. Jiayang Zhang
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  2. Huashan Feng
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  3. Yebiao Zhang
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  4. Lefan Kang
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Corresponding author

Correspondence to Jiayang Zhang .

Editor information

Editors and Affiliations

  1. National University of Singapore, Singapore, Singapore

    Shuzhi Sam Ge

  2. Air Force Medical University, Xi'an, China

    Zhuojing Luo

  3. Northwestern Polytechnical University, Xi'an, China

    Yanen Wang

  4. University of the Arts London, London, UK

    Hooman Samani

  5. National University of Singapore, Singapore, Singapore

    Ruihang Ji

  6. The University of Alabama, Tuscaloosa, AL, USA

    Hongsheng He

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Zhang, J., Feng, H., Zhang, Y., Kang, L. (2025). Advancing Closed-Chain Robot Control Through Model Predictive Techniques and Virtual Breakpoints. In: Ge, S.S., Luo, Z., Wang, Y., Samani, H., Ji, R., He, H. (eds) Social Robotics. ICSR + BioMed 2024. Lecture Notes in Computer Science(), vol 14916. Springer, Singapore. https://doi.org/10.1007/978-981-97-8963-4_1

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  • DOI: https://doi.org/10.1007/978-981-97-8963-4_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-8962-7

  • Online ISBN: 978-981-97-8963-4

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