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NMPC-based Obstacle Avoidance and Whole-body Motion Planning for Mobile Manipulator

모바일 매니퓰레이터의 NMPC 기반 장애물 회피 및 전신 모션 플래닝

  • Kim, Sunhong (Department of Electrical and Electronic Engineering, Hanyang University) ;
  • Sathya, Ajay (Mechatronics, Robotics, and Automation Engineering, KU Leuven) ;
  • Swevers, Jan (Mechanical Engineering, KU Leuven) ;
  • Choi, Youngjin (Department of Electrical and Electronic Engineering, Hanyang University ERICA)
  • Received : 2022.03.24
  • Accepted : 2022.04.22
  • Published : 2022.08.31

Abstract

This study presents a nonlinear model predictive control (NMPC)-based obstacle avoidance and whole-body motion planning method for the mobile manipulators. For the whole-body motion control, the mobile manipulator with an omnidirectional mobile base was modeled as a nine degrees-of-freedom (DoFs) serial open chain with the PPR (base) plus 6R (arm) joints, and a swept sphere volume (SSV) was applied to define a convex hull for collision avoidance. The proposed receding horizon control scheme can generate a trajectory to track the end-effector pose while avoiding the self-collision and obstacle in the task space. The proposed method could be calculated using an interior-point (IP) method solver with 100[ms] sampling time and ten samples of horizon size, and the validation of the method was conducted in the environment of Pybullet simulation.

Keywords

Acknowledgement

This research was supported by the Ministry of Trade, Industry, and Energy in Korea, under the Fostering Global Talents for Innovative Growth Program (P0008745) and the Technology Innovation Program (20008908) supervised by the Korea Institute for Advancement of Technology (KIAT), Republic of Korea

References

  1. H. Zhang, Y. Jia, Y. Guo, K. Qian, A. Song, and N. Xi, "Online sensor information and redundancy resolution based obstacle avoidance for high dof mobile manipulator teleoperation," International Journal of Advanced Robotic Systems, vol. 10, no. 5, May, 2013, DOI:10.5772/56470.
  2. D. H. Shin, B. S. Hamner, S. Singh, and M. Hwangbo, "Motion planning for a mobile manipulator with imprecise locomotion," 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453), Las Vegas, NV, USA, 2003, DOI: 10.1109/IROS.2003.1250735.
  3. J. Pankert and M. Hutter. "Perceptive model predictive control for continuous mobile manipulation," IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 6177-6184, Oct., 2020, DOI: 10.1109/LRA.2020.3010721.
  4. J. Kindle, F. Furrer, T. Novkovic, J. J. Chung, R. Siegwart, and J. Nieto, "Whole-body control of a mobile manipulator using end-to-end reinforcement learning," arXiv preprint arXiv:2003.02637, 2020, DOI: 10.48550/arXiv.2003.02637.
  5. A. H. Khan, S. Li, D. Chen, and L. Liao, "Tracking control of redundant mobile manipulator: An RNN based metaheuristic approach," Neurocomputing, vol. 400, no. 4, pp. 272-284, Aug., 2020, DOI: 10.1016/j.neucom.2020.02.109.
  6. E. F. Camacho and C. B. Alba., "Chapter 1 Introduction to Model Predictive Control," Model Predictive Control, Springer science & business media, 2013, DOI: 10.1007/978-0-85729-398-5.
  7. M. V. Minniti, F. Farshidian, R. Grandia, and M. Hutter, "WholeBody MPC for a Dynamically Stable Mobile Manipulator," IEEE Robotics and Automation Letters, vol. 4, no.4, pp. 3687-3694, Jul., 2019, DOI: 10.1109/LRA.2019.2927955.
  8. Z. Li, J. Deng, R. Lu, Y. Xu, J. Bai, and C. Y. Su, "Trajectory-tracking control of mobile robot systems incorporating neural-dynamic optimized model predictive approach," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 46, no. 6, pp. 740-749, Aug., 2015, DOI: 10.1109/TSMC.2015.2465352.
  9. K. M. Lynch, and F. C. Park, "Chapter 13 Wheeled Mobile Robots", Modern Robotics, Cambridge University Press, 2017, [Online], https://www.amazon.com/Modern-Robotics-Mechanics-Planning-Control/dp/1107156300.
  10. M. Tang, Y. J. Kim, and D. Manocha. "C2A: Controlled conservative advancement for continuous collision detection of polygonal models," 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan, 2009, DOI: 10.1109/ROBOT.2009.5152234.
  11. M. Kramer, C. Rosmann, F. Hoffmann, and T. Bertram, "Model predictive control of a collaborative manipulator considering dynamic obstacles," Optimal Control Applications and Methods, vol. 41, no. 4, pp. 1211-1232, Apr., 2020, DOI: 10.1002/oca.2599.
  12. V. J. Lumelsky, "On fast computation of distance between line segments," Information Processing Letters, vol. 21 no. 2, pp. 55-61, Aug., 1985, DOI: 10.1016/0020-0190(85)90032-8.
  13. A. S. Sathya, J. Gillis, G. Pipeleers, and J. Swevers, "Real-time robot arm motion planning and control with nonlinear model predictive control using augmented lagrangian on a first-order solver," 2020 European Control Conference (ECC), St. Petersburg, Russia, pp. 507-512, 2020, DOI: 10.23919/ECC51009.2020.9143732.
  14. J.-H. Bae, J.-H. Park, Y. Oh, D. Kim, Y. Choi, and W. Yang, "Task space control considering passive muscle stiffness for redundant robotic arms," Intelligent Service Robotics, vol. 8, no. 2, pp. 93-104, Jan., 2015, DOI: 10.1007/s11370-015-0165-2.
  15. J. Fiala and B. Marteau, Nonlinear Optimization: A Comparison of Two Competing Approaches Active-set SQP vs IPM, 2017, [Online], https://www.nag.com/market/nonlinear-optimization-comparison.pdf.
  16. A. Wachter and L.T. Biegler. "On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming," Mathematical Programming, vol. 106, no. 1 pp. 25-57, Apr., 2006, DOI: 10.1007/s10107-004-0559-y.
  17. J. Carpentier, G. Saurel, G. Buondonno, J. Mirabel, F. Lamiraux, O. Stasse, and N. Mansard, "The Pinocchio C++ library: A fast and flexible implementation of rigid body dynamics algorithms and their analytical derivatives," 2019 IEEE/SICE International Symposium on System Integration (SII), Paris, France, 2019, DOI: 10.1109/SII.2019.8700380.
  18. M. Mittal, D. Hoeller, F. Farshidian, M. Hutter, and A. Garg, "Articulated object interaction in unknown scenes with whole-body mobile manipulation," arXiv preprint arXiv:2103.10534, 2021, DOI: 10.48550/arXiv.2103.10534.