Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
119 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Optimizing Dynamic Balance in a Rat Robot via the Lateral Flexion of a Soft Actuated Spine (2403.00944v1)

Published 1 Mar 2024 in cs.RO

Abstract: Balancing oneself using the spine is a physiological alignment of the body posture in the most efficient manner by the muscular forces for mammals. For this reason, we can see many disabled quadruped animals can still stand or walk even with three limbs. This paper investigates the optimization of dynamic balance during trot gait based on the spatial relationship between the center of mass (CoM) and support area influenced by spinal flexion. During trotting, the robot balance is significantly influenced by the distance of the CoM to the support area formed by diagonal footholds. In this context, lateral spinal flexion, which is able to modify the position of footholds, holds promise for optimizing balance during trotting. This paper explores this phenomenon using a rat robot equipped with a soft actuated spine. Based on the lateral flexion of the spine, we establish a kinematic model to quantify the impact of spinal flexion on robot balance during trot gait. Subsequently, we develop an optimized controller for spinal flexion, designed to enhance balance without altering the leg locomotion. The effectiveness of our proposed controller is evaluated through extensive simulations and physical experiments conducted on a rat robot. Compared to both a non-spine based trot gait controller and a trot gait controller with lateral spinal flexion, our proposed optimized controller effectively improves the dynamic balance of the robot and retains the desired locomotion during trotting.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (30)
  1. D. Gong, J. Yan, and G. Zuo, “A review of gait optimization based on evolutionary computation,” Applied Computational Intelligence and Soft Computing, vol. 2010, 2010.
  2. S. Xiao, Z. Bing, K. Huang, and Y. Huang, “Snake-like robot climbs inside different pipes,” in 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO).   IEEE, 2017, pp. 1232–1239.
  3. Z. Bing, C. Lemke, F. O. Morin, Z. Jiang, L. Cheng, K. Huang, and A. Knoll, “Perception-action coupling target tracking control for a snake robot via reinforcement learning,” Frontiers in Neurorobotics, vol. 14, p. 591128, 2020.
  4. Z. Bing, A. E. Sewisy, G. Zhuang, F. Walter, F. O. Morin, K. Huang, and A. Knoll, “Toward cognitive navigation: Design and implementation of a biologically inspired head direction cell network,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 5, pp. 2147–2158, 2021.
  5. Z. Bing, H. Zhou, R. Li, X. Su, F. O. Morin, K. Huang, and A. Knoll, “Solving robotic manipulation with sparse reward reinforcement learning via graph-based diversity and proximity,” IEEE Transactions on Industrial Electronics, vol. 70, no. 3, pp. 2759–2769, 2022.
  6. H. Zhou, X. Yao, Y. Meng, S. Sun, Z. Bing, K. Huang, and A. Knoll, “Language-conditioned learning for robotic manipulation: A survey,” arXiv preprint arXiv:2312.10807, 2023.
  7. M. Wang, Z. Bing, X. Yao, S. Wang, H. Kai, H. Su, C. Yang, and A. Knoll, “Meta-reinforcement learning based on self-supervised task representation learning,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 8, 2023, pp. 10 157–10 165.
  8. Z. Bing, M. Brucker, F. O. Morin, R. Li, X. Su, K. Huang, and A. Knoll, “Complex robotic manipulation via graph-based hindsight goal generation,” IEEE Transactions on neural networks and learning systems, vol. 33, no. 12, pp. 7863–7876, 2021.
  9. A. W. Winkler, C. Mastalli, I. Havoutis, M. Focchi, D. G. Caldwell, and C. Semini, “Planning and execution of dynamic whole-body locomotion for a hydraulic quadruped on challenging terrain,” in 2015 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2015, pp. 5148–5154.
  10. P. Biswal and P. K. Mohanty, “Development of quadruped walking robots: A review,” Ain Shams Engineering Journal, vol. 12, no. 2, pp. 2017–2031, 2021.
  11. Z. Bing, D. Lerch, K. Huang, and A. Knoll, “Meta-reinforcement learning in non-stationary and dynamic environments,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 3, pp. 3476–3491, 2022.
  12. Z. Zhang, Y. Huang, Z. Zhao, Z. Bing, A. Knoll, and K. Huang, “A hierarchical reinforcement learning approach for adaptive quadruped locomotion of a rat robot,” in 2023 IEEE International Conference on Robotics and Biomimetics (ROBIO).   IEEE, 2023, pp. 1–6.
  13. Z. Bing, L. Knak, L. Cheng, F. O. Morin, K. Huang, and A. Knoll, “Meta-reinforcement learning in nonstationary and nonparametric environments,” IEEE Transactions on Neural Networks and Learning Systems, 2023.
  14. B. Ugurlu, K. Kotaka, and T. Narikiyo, “Actively-compliant locomotion control on rough terrain: Cyclic jumping and trotting experiments on a stiff-by-nature quadruped,” in 2013 IEEE international conference on robotics and automation.   IEEE, 2013, pp. 3313–3320.
  15. Y. Jia, X. Luo, B. Han, G. Liang, J. Zhao, and Y. Zhao, “Stability criterion for dynamic gaits of quadruped robot,” Applied Sciences, vol. 8, no. 12, p. 2381, 2018.
  16. J. He, J. Shao, G. Sun, and X. Shao, “Survey of quadruped robots coping strategies in complex situations,” Electronics, vol. 8, no. 12, p. 1414, 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 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2018, pp. 2245–2252.
  18. Z. Bing, A. Rohregger, F. Walter, Y. Huang, P. Lucas, F. O. Morin, K. Huang, and A. Knoll, “Lateral flexion of a compliant spine improves motor performance in a bioinspired mouse robot,” Science Robotics, vol. 8, no. 85, p. eadg7165, 2023.
  19. M. Vukobratović and B. Borovac, “Zero-moment point—thirty five years of its life,” International journal of humanoid robotics, vol. 1, no. 01, pp. 157–173, 2004.
  20. B. Ugurlu, I. Havoutis, C. Semini, and D. G. Caldwell, “Dynamic trot-walking with the hydraulic quadruped robot—hyq: Analytical trajectory generation and active compliance control,” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.   IEEE, 2013, pp. 6044–6051.
  21. G. Chen, S. Guo, B. Hou, and J. Wang, “Virtual model control for quadruped robots,” IEEE Access, vol. 8, pp. 140 736–140 751, 2020.
  22. H. Diedam, D. Dimitrov, P.-B. Wieber, K. Mombaur, and M. Diehl, “Online walking gait generation with adaptive foot positioning through linear model predictive control,” in 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.   IEEE, 2008, pp. 1121–1126.
  23. 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.
  24. W. Sun, X. Tian, Y. Song, B. Pang, X. Yuan, and Q. Xu, “Balance control of a quadruped robot based on foot fall adjustment,” Applied Sciences, vol. 12, no. 5, p. 2521, 2022.
  25. H. Chen, Z. Hong, S. Yang, P. M. Wensing, and W. Zhang, “Quadruped capturability and push recovery via a switched-systems characterization of dynamic balance,” IEEE Transactions on Robotics, 2023.
  26. T. R. Ham, M. Farrag, A. M. Soltisz, E. H. Lakes, K. D. Allen, and N. D. Leipzig, “Automated gait analysis detects improvements after intracellular σ𝜎\sigmaitalic_σ peptide administration in a rat hemisection model of spinal cord injury,” Annals of biomedical engineering, vol. 47, pp. 744–753, 2019.
  27. Y. Huang, Z. Bing, Z. Zhang, K. Huang, F. O. Morin, and A. Knoll, “Smooth stride length change of rat robot with a compliant actuated spine based on cpg controller,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2023, pp. 331–338.
  28. D. Chen, N. Li, H. Wang, and L. Chen, “Effect of flexible spine motion on energy efficiency in quadruped running,” Journal of Bionic Engineering, vol. 14, no. 4, pp. 716–725, 2017.
  29. S. Bhattacharya, A. Singla, D. Dholakiya, S. Bhatnagar, B. Amrutur, A. Ghosal, S. Kolathaya, et al., “Learning active spine behaviors for dynamic and efficient locomotion in quadruped robots,” in 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN).   IEEE, 2019, pp. 1–6.
  30. Y. Huang, Z. Bing, F. Walter, A. Rohregger, Z. Zhang, K. Huang, F. O. Morin, and A. Knoll, “Enhanced quadruped locomotion of a rat robot based on the lateral flexion of a soft actuated spine,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2022, pp. 2622–2627.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (6)
  1. Yuhong Huang (8 papers)
  2. Zhenshan Bing (39 papers)
  3. Zitao Zhang (13 papers)
  4. Genghang Zhuang (5 papers)
  5. Kai Huang (146 papers)
  6. Alois Knoll (190 papers)
Citations (1)
View on Semantic Scholar

Summary

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

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