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A heuristic gait template planning and dynamic motion control for biped robots

Published online by Cambridge University Press:  15 November 2022

Lianqiang Han
Affiliation:
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
Xuechao Chen*
Affiliation:
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
Zhangguo Yu
Affiliation:
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
Zhifa Gao
Affiliation:
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
Gao Huang
Affiliation:
Faculty of Information Technology, Beijing University of Technology, Beijing, China
Jintao Zhang
Affiliation:
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
Kenji Hashimoto
Affiliation:
Department of Mechanical Engineering Informatics, Meiji University/Humanoid Robotics Institute (HRI), Waseda University, Kanagawa, Japan
Qiang Huang
Affiliation:
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
*
*Corresponding author. E-mail: chenxuechao@bit.edu.cn

Abstract

Biped robots with dynamic motion control have shown strong robustness in complex environments. However, many motion planning methods rely on models, which have difficulty dynamically modifying the walking cycle, height, and other gait parameters to cope with environmental changes. In this study, a heuristic model-free gait template planning method with dynamic motion control is proposed. The gait trajectory can be generated by inputting the desired speed, walking cycle, and support height without a model. Then, the stable walking of the biped robot can be realized by foothold adjustment and whole-body dynamics model control. The gait template can be changed in real time to achieve gait flexibility of the biped robot. Finally, the effectiveness of the method is verified by simulations and experiments of the biped robot BHR-B2. The research presented here helps improve the gait transition ability of biped robots in dynamic locomotion.

Type
Research Article
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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References

Namiki, A. and Yokosawa, S., “Origami folding by multifingered hands with motion primitives,” Cyborg Bionic Syst. 2021, 115 (2021).CrossRefGoogle ScholarPubMed
Chen, X., Yu, Z., Zhang, W., Zheng, Y., Huang, Q. and Ming, A., “Bioinspired control of walking with toe-off, heel-strike, and disturbance rejection for a biped robot,” IEEE Trans. Ind. Electron. 64(10), 79627971 (2017).CrossRefGoogle Scholar
Huang, K., Xian, Y., Zhen, S. and HSun, “Robust control design for a planar humanoid robot arm with high strength composite gear and experimental validation,” Mech. Syst. Signal. Process. 155, 107442 (2021).CrossRefGoogle Scholar
Zeng, C., Su, H., Li, Y., Guo, J. and Yang, C., “An approach for robotic leaning inspired by biomimetic adaptive control,” IEEE Trans. Industr. Inform. 18(3), 14791488 (2021).CrossRefGoogle Scholar
Fukuda, T., “Cyborg and bionic systems: Signposting the future,” Cyborg Bionic Syst. 2020, 12 (2020).CrossRefGoogle Scholar
Boston Dynamics, “ATLAS” , Available at: www.bostondynamics.com/atlas, accessed on 15 June 2022.Google Scholar
Liu, C., Yang, J., An, K., Liu, M. and Chen, Q., “Robust control of semi-passive biped dynamic locomotion based on a discrete control lyapunov function,” Robotica 38(8), 13451358 (2020).CrossRefGoogle Scholar
Kim, D., Zhao, Y., Thomas, G., Fernandez, B. R. and Sentis, L., “Stabilizing series-elastic point-foot bipeds using whole-body operational space control,” IEEE Trans. Robot. 32(6), 13621379 (2016).CrossRefGoogle Scholar
Siekmann, J., Green, K. and Warila, J., “Blind bipedal stair traversal via sim-to-real reinforcement learning,” arXiv preprint arXiv: 2105.08328, 2021.Google Scholar
Agility Robotics, “Digit tackles wet and muddy hills”, Available at: www.youtube.com/watch?v=bV3KnthEY2c, accessed on 15 June 2022.Google Scholar
Da, X., Hartley, R. and Grizzle, J. W., “Supervised Learning for Stabilizing Underactuated Bipedal Robot Locomotion, with Outdoor Experiments on the Wave Field,” In: IEEE International Conference on Robotics and Automation IEEE (2017) pp. 34763483.Google Scholar
Kajita, S., Kanehiro, F. and Kaneko, K., “Biped Walking Pattern Generation by Using Preview Control of Zero-Moment Point,” In: IEEE International Conference on Robotics and Automation IEEE(2003) pp. 16201626.Google Scholar
Luo, J., Su, Y., Ruan, L., Zhao, Y., Kim, D., Sentis, L. and Fu, C. L., “Robust bipedal locomotion based on a hierarchical control structure,” Robotica 37(10), 17501767 (2019).CrossRefGoogle Scholar
Mesesan, G., Englsberger, J. and Garofalo, G., “Dynamic Walking on Compliant and Uneven Terrain Using DCM and Passivity-Based Whole-Body Control,” In: IEEE-RAS 19th International Conference on Humanoid Robots IEEE (2019) pp. 2532.Google Scholar
Pi, M., Kang, Y., Xu, C., Li, G. and Li, Z. J., “Adaptive time-delay balance control of biped robots,” IEEE Trans. Ind. Electron. 67(4), 29362944 (2019).CrossRefGoogle Scholar
Xiong, X. and Ames, A., “Orbit Characterization, Stabilization and Composition on 3D Underactuated Bipedal Walking via Hybrid Passive Linear Inverted Pendulum Model,” In: IEEE/RSJ International Conference on Intelligent Robots and Systems IEEE (2019) pp. 46444651.Google Scholar
Xiong, X. and Ames, A., “SLIP walking over rough terrain via H-LIP stepping and backstepping-barrier function inspired quadratic program,” IEEE Robot. Autom. Lett. 6(2), 21222129 (2021).CrossRefGoogle Scholar
Dadashzadeh, B. and Macnab, C. J., “Slip-based control of bipedal walking based on two-level control strategy,” Robotica 38(8), 14341449 (2020).CrossRefGoogle Scholar
Hereid, A., Harib, O., Hartley, R., Gong, Y. and Grizzle, J. W., “Rapid Trajectory Optimization Using c-Frost with Illustration on a Cassie-Series Dynamic Walking Biped,” In: IEEE/RSJ International Conference on Intelligent Robots and Systems IEEE (2019) pp. 47224729.Google Scholar
Hobon, M., De-León-Gómez, V., Abba, G., Aoustin, Y. and Chevallereau, C., “Feasible speeds for two optimal periodic walking gaits of a planar biped robot,” Robotica 40(2), 377402 (2022).CrossRefGoogle Scholar
Drnach, L. and Zhao, Y., “Robust trajectory optimization over uncertain terrain with stochastic complementarity,” IEEE Robot. Autom. Lett. 6(2), 11681175 (2021).CrossRefGoogle Scholar
Scott, K., Robin, D. and Maurice, F., “Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot,” Auton. Robot. 40(3), 429455 (2016).Google Scholar
Grizzle, J. W., Christine, C. and Shih, C. L., “HZD-Based Control of a Five-Link Underactuated 3D Bipedal Robot,” In: IEEE Conference on Decision and Control IEEE (2008) pp. 52065213.Google Scholar
Raibert, M. H.. Legged Robots That Balance (MIT Press, USA, 1986).CrossRefGoogle Scholar
Nelson, G., Saunders, A. and Playter, R., “The PETMAN and Atlas Robots at Boston Dynamics,” In: Humanoid Robotics: A Reference, Goswami, A. and Vadakkepat, P., Springer, Dordrecht, (2019) pp. 169186.CrossRefGoogle Scholar
Han, L., Chen, X., Yu, Z., Zhu, X., Hashimoto, K. and Huang, Q., “Trajectory-free dynamic locomotion using key trend states for biped robots with point feet,” Sci. China Inf. Sci., (2022). doi: 10.1007/s11432-021-3450-5.CrossRefGoogle Scholar
Han, L., Chen, X., Yu, Z., Gao, Z., Huang, Y. and Huang, Q., “Balance control of underactuated biped robot for discrete terrain,” Acta Autom. Sin. 48(9), 111 (2022).Google Scholar
Yin, K., Kevin, L. and Michiel, V., “Simbicon: Simple biped locomotion control,” ACM Trans. Graph. 26(3), 105 (2007).CrossRefGoogle Scholar
Qi, W. and Su, H., “A cybertwin based multimodal network for ECG patterns monitoring using deep learning,” IEEE Trans. Ind. Inform. 18(10), 66636670 (2022).CrossRefGoogle Scholar
Su, H., Hu, Y., Karimi, H. R., Knoll, A., Ferrigno, G. and De Momi, E., “Improved recurrent neural network-based manipulator control with remote center of motion constraints: Experimental results,” Neural Netw. 131, 291299 (2020).CrossRefGoogle ScholarPubMed
Li, Z., Cheng, X., Peng, X., Abbeel, P., Levine, S., Berseth, G. and Sreenath, K., “Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots,” In: IEEE International Conference on Robotics and Automation (ICRA) IEEE (2021) pp. 28112817.Google Scholar
Krishna, L., Castillo, G. A., Mishra, U. A., Hereid, A. and Kolathaya, S., “Linear policies are sufficient to realize robust bipedal walking on challenging terrains,” IEEE Robot. Autom. Lett. 7(2), 20472054 (2022).CrossRefGoogle Scholar
Zhu, X., Wang, L., Yu, Z., Chen, X. and Han, L., “Motion Control for Underactuated Robots Adaptable to Uneven Terrain by Decomposing Body Balance and Velocity Tracking,” In: IEEE International Conference on Advanced Robotics and Mechatronics (ICARM) IEEE (2021) pp. 729734.Google Scholar
Fengy, S. Y., Whitmanz, E., Xinjilefuy, X. and Atkeson, C. G., “Optimization Based Full Body Control for the Atlas Robot,” In: IEEE-RAS International Conference on Humanoid Robots (Humanoids) IEEE (2014) pp. 120127.Google Scholar
Su, H., Qi, W., Schmirander, Y., Ovur, S., Cai, S. and Xiong, X., “A human activity-aware shared control solution for medical human-robot interaction,” Assembly Autom. 42(3), 388394 (2022).CrossRefGoogle Scholar