Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
144 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

CDM-MPC: An Integrated Dynamic Planning and Control Framework for Bipedal Robots Jumping (2405.11773v1)

Published 20 May 2024 in cs.RO

Abstract: Performing acrobatic maneuvers like dynamic jumping in bipedal robots presents significant challenges in terms of actuation, motion planning, and control. Traditional approaches to these tasks often simplify dynamics to enhance computational efficiency, potentially overlooking critical factors such as the control of centroidal angular momentum (CAM) and the variability of centroidal composite rigid body inertia (CCRBI). This paper introduces a novel integrated dynamic planning and control framework, termed centroidal dynamics model-based model predictive control (CDM-MPC), designed for robust jumping control that fully considers centroidal momentum and non-constant CCRBI. The framework comprises an optimization-based kinodynamic motion planner and an MPC controller for real-time trajectory tracking and replanning. Additionally, a centroidal momentum-based inverse kinematics (IK) solver and a landing heuristic controller are developed to ensure stability during high-impact landings. The efficacy of the CDM-MPC framework is validated through extensive testing on the full-sized humanoid robot KUAVO in both simulations and experiments.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (37)
  1. M. Chignoli, D. Kim, E. Stanger-Jones, and S. Kim, “The mit humanoid robot: Design, motion planning, and control for acrobatic behaviors,” in IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2021.
  2. T. Zhu, J. Hooks, and D. Hong, “Design, modeling, and analysis of a liquid cooled proprioceptive actuator for legged robots,” in IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2019.
  3. H. Dai, A. Valenzuela, and R. Tedrake, “Whole-body motion planning with centroidal dynamics and full kinematics,” in IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2014.
  4. A. Herzog, S. Schaal, and L. Righetti, “Structured contact force optimization for kino-dynamic motion generation,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016.
  5. A. Saccon, S. Traversaro, F. Nori, and H. Nijmeijer, “On centroidal dynamics and integrability of average angular velocity,” IEEE Robotics and Automation Letters (RA-L), vol. 2, no. 2, pp. 943–950, 2017.
  6. J. Zhang, J. Shen, Y. Liu, and D. W. Hong, “Design of a jumping control framework with heuristic landing for bipedal robots,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023.
  7. P. M. Wensing, M. Posa, Y. Hu, A. Escande, N. Mansard, and A. Del Prete, “Optimization-based control for dynamic legged robots,” IEEE Transactions on Robotics (T-RO), vol. 40, pp. 43–63, 2024.
  8. H. Qi, X. Chen, Z. Yu, G. Huang, Y. Liu, L. Meng, and Q. Huang, “Vertical jump of a humanoid robot with cop-guided angular momentum control and impact absorption,” IEEE Transactions on Robotics (T-RO), vol. 39, no. 4, pp. 3154–3166, 2023.
  9. Z. Li, X. B. Peng, P. Abbeel, S. Levine, G. Berseth, and K. Sreenath, “Robust and versatile bipedal jumping control through reinforcement learning,” arXiv preprint arXiv:2302.09450, 2023.
  10. G. Mesesan, R. Schuller, J. Englsberger, C. Ott, and A. Albu-Schäffer, “Unified motion planner for walking, running, and jumping using the three-dimensional divergent component of motion,” IEEE Transactions on Robotics (T-RO), 2023.
  11. A. SaLoutos, E. Stanger-Joncs, Y. Ding, M. Chignoli, and S. Kim, “Design and development of the mit humanoid: A dynamic and robust research platform,” in IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2023.
  12. K. Zhou, P. Wu, Y. Su, H. Gao, J. Ma, H. Liu, and C. Liu, “Aspire: An informative trajectory planner with mutual information approximation for target search and tracking,” in IEEE International Conference on Robotics and Automation (ICRA), 2024.
  13. H. Gao, P. Wu, Y. Su, K. Zhou, J. Ma, H. Liu, and C. Liu, “Probabilistic visibility-aware trajectory planning for target tracking in cluttered environments,” in American Control Conference (ACC), 2024.
  14. Y. Su, J. Zhang, Z. Jiao, H. Li, M. Wang, and H. Liu, “Real-time dynamic-consistent motion planning for over-actuated uavs,” in IEEE International Conference on Robotics and Automation (ICRA), 2024.
  15. S. Kuindersma, R. Deits, M. Fallon, A. Valenzuela, H. Dai, F. Permenter, T. Koolen, P. Marion, and R. Tedrake, “Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot,” Autonomous robots, vol. 40, no. 3, pp. 429–455, 2016.
  16. J. Luo, Y. Su, L. Ruan, Y. Zhao, D. Kim, L. Sentis, and C. Fu, “Robust bipedal locomotion based on a hierarchical control structure,” Robotica, vol. 37, no. 10, pp. 1750–1767, 2019.
  17. G. Bledt, M. J. Powell, B. Katz, J. Di Carlo, P. M. Wensing, and S. Kim, “Mit cheetah 3: Design and control of a robust, dynamic quadruped robot,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018.
  18. D. Kim, J. Di Carlo, B. Katz, G. Bledt, and S. Kim, “Highly dynamic quadruped locomotion via whole-body impulse control and model predictive control,” arXiv preprint arXiv:1909.06586, 2019.
  19. C. Mastalli, W. Merkt, G. Xin, J. Shim, M. Mistry, I. Havoutis, and S. Vijayakumar, “Agile maneuvers in legged robots: a predictive control approach,” arXiv preprint arXiv:2203.07554, 2022.
  20. Z. Zhou, B. Wingo, N. Boyd, S. Hutchinson, and Y. Zhao, “Momentum-aware trajectory optimization and control for agile quadrupedal locomotion,” IEEE Robotics and Automation Letters (RA-L), vol. 7, no. 3, pp. 7755–7762, 2022.
  21. M. Wang, Y. Su, H. Liu, and Y. Xu, “Walkingbot: Modular interactive legged robot with automated structure sensing and motion planning,” in IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 2020.
  22. X. Lin, J. Zhang, J. Shen, G. Fernandez, and D. W. Hong, “Optimization based motion planning for multi-limbed vertical climbing robots,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019.
  23. J. Pratt, J. Carff, S. Drakunov, and A. Goswami, “Capture point: A step toward humanoid push recovery,” in IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2006.
  24. N. Scianca, D. De Simone, L. Lanari, and G. Oriolo, “Mpc for humanoid gait generation: Stability and feasibility,” IEEE Transactions on Robotics (T-RO), vol. 36, no. 4, pp. 1171–1188, 2020.
  25. P. Kryczka, P. Kormushev, N. G. Tsagarakis, and D. G. Caldwell, “Online regeneration of bipedal walking gait pattern optimizing footstep placement and timing,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015.
  26. R. Budhiraja, J. Carpentier, and N. Mansard, “Dynamics consensus between centroidal and whole-body models for locomotion of legged robots,” in IEEE International Conference on Robotics and Automation (ICRA), 2019.
  27. B. Ponton, M. Khadiv, A. Meduri, and L. Righetti, “Efficient multicontact pattern generation with sequential convex approximations of the centroidal dynamics,” IEEE Transactions on Robotics (T-RO), vol. 37, no. 5, pp. 1661–1679, 2021.
  28. T. Kwon, Y. Lee, and M. Van De Panne, “Fast and flexible multilegged locomotion using learned centroidal dynamics,” ACM Transactions on Graphics (TOG), vol. 39, no. 4, pp. 46–1, 2020.
  29. R. Grandia, F. Jenelten, S. Yang, F. Farshidian, and M. Hutter, “Perceptive locomotion through nonlinear model-predictive control,” IEEE Transactions on Robotics (T-RO), 2023.
  30. A. Meduri, P. Shah, J. Viereck, M. Khadiv, I. Havoutis, and L. Righetti, “Biconmp: A nonlinear model predictive control framework for whole body motion planning,” IEEE Transactions on Robotics (T-RO), vol. 39, no. 2, pp. 905–922, 2023.
  31. J. Luo, Z. Gong, Y. Su, L. Ruan, Y. Zhao, H. H. Asada, and C. Fu, “Modeling and balance control of supernumerary robotic limb for overhead tasks,” IEEE Robotics and Automation Letters (RA-L), vol. 6, no. 2, pp. 4125–4132, 2021.
  32. P. E. Gill, W. Murray, and M. A. Saunders, “Snopt: An sqp algorithm for large-scale constrained optimization,” SIAM review, vol. 47, no. 1, pp. 99–131, 2005.
  33. D. E. Orin, A. Goswami, and S.-H. Lee, “Centroidal dynamics of a humanoid robot,” Autonomous robots, vol. 35, no. 2-3, pp. 161–176, 2013.
  34. S.-H. Lee and A. Goswami, “Reaction mass pendulum (rmp): An explicit model for centroidal angular momentum of humanoid robots,” in IEEE International Conference on Robotics and Automation (ICRA), 2007.
  35. S. Feng, E. Whitman, X. Xinjilefu, and C. G. Atkeson, “Optimization based full body control for the atlas robot,” in IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2014.
  36. I. Vatavuk and Z. Kovačić, “Precise jump planning using centroidal dynamics based bilevel optimization,” in IEEE International Conference on Robotics and Automation (ICRA), 2021.
  37. R. Tedrake and the Drake Development Team, “Drake: Model-based design and verification for robotics,” 2019.
Citations (6)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now