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Gait optimization and energy-based stability for biped locomotion using large-scale programming

Published online by Cambridge University Press:  17 April 2023

Ye Xie
Affiliation:
Intelligent Robot Research Centre, Zhejiang Lab, Hangzhou, Zhejiang, 311100, China
Chengzhi Gao
Affiliation:
Intelligent Robot Research Centre, Zhejiang Lab, Hangzhou, Zhejiang, 311100, China
Shiqiang Zhu
Affiliation:
Intelligent Robot Research Centre, Zhejiang Lab, Hangzhou, Zhejiang, 311100, China Zhejiang University, Hangzhou, Zhejiang, 311100, China
Xufei Yan
Affiliation:
Intelligent Robot Research Centre, Zhejiang Lab, Hangzhou, Zhejiang, 311100, China
Lingyu Kong*
Affiliation:
Intelligent Robot Research Centre, Zhejiang Lab, Hangzhou, Zhejiang, 311100, China
Anhuan Xie
Affiliation:
Intelligent Robot Research Centre, Zhejiang Lab, Hangzhou, Zhejiang, 311100, China Zhejiang University, Hangzhou, Zhejiang, 311100, China
Jason Gu
Affiliation:
Intelligent Robot Research Centre, Zhejiang Lab, Hangzhou, Zhejiang, 311100, China Dalhousie University, Halifax, B3H 4R2, Canada
Dan Zhang
Affiliation:
York University, Toronto, M3J 1P3, Canada
*
*Corresponding author. E-mail: kongly@zhejianglab.edu.cn

Abstract

This paper presents a gait optimization method to generate the locomotion pattern for biped and discuss its stability. The main contribution of this paper is a newly proposed energy-based stability criterion, which permits the dynamic stable walking and could be straight-forwardly generalized to different locomotion scenarios and biped robots. The gait optimization problem is formulated subject to the constraints of the whole-body dynamics and kinematics. The constraints are established based on the modelling of bipedal hybrid dynamical systems. Following the whole-body modelling, the system energy is acquired and then applied to create the stability criterion. The optimization objective is also established on the system energy. The gait optimization is solved by being converted to a large-scale programming problem, where the transcription accuracy is improved via the spectral method. To further reduce the dimensionality of the large-scale problem, the whole-body dynamics is re-constructed. The generalization of the optimized gait is improved by the design of feedback control. The optimization examples demonstrate that the stability criterion naturally leads to a cyclic biped locomotion, though the periodicity was not previously imposed. Two simulation cases, level ground walking and slope walking, verify the generalization of the stability criterion and feedback control. The stability analyses are carried out by investigating the motions of centre of gravity and centre of pressure. It is revealed that if the tracked speed is above 0.3 m/s or the biped accelerates/climbs the slope, the stability criterion accomplishes the dynamic stable walking, where zero moment point criterion is not strictly complied.

Type
Research Article
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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References

Pratt, J., Carff, J., Drakunov, S. and Goswami, A., “Capture Point: A Step toward Humanoid Push Recovery,” In: 2006 6th IEEE-RAS International Conference on Humanoid Robots (2006) pp. 200207.Google Scholar
Chevallereau, C. and Aoustin, Y., “Optimal reference trajectories for walking and running of a biped robot,” Robotica 19(5), 557569 (2001).CrossRefGoogle Scholar
Semwal, V. B., Lalwani, P., Mishra, M. K., Bijalwan, V. and Chadha, J. S., “An optimized feature selection using bio-geography optimization technique for human walking activities recognition,” Computing 103(12), 28932914 (2021).CrossRefGoogle Scholar
Semwal, V. B., Kumar, C., Mishra, P. K. and Nandi, G. C., “Design of vector field for different subphases of gait and regeneration of gait pattern,” IEEE Trans. Autom. Sci. Eng. 15(1), 104110 (2018).CrossRefGoogle Scholar
Semwal, V. B. and Nandi, G. C., “Generation of joint trajectories using hybrid automate-based model: A rocking block-based approach,” IEEE Sens. J. 16(14), 58055816 (2016).CrossRefGoogle Scholar
Chow, C. K. and Jacobson, D. H., “Studies of human locomotion via optimal programming,” Math. Biosci. 10(3-4), 239306 (1971).CrossRefGoogle Scholar
Bessonnet, G., Chessé, S. and Sardain, P., “Optimal gait synthesis of a seven-link planar biped,” Int. J. Rob. Res. 23(10-11), 10591073 (2004).CrossRefGoogle Scholar
Selim, E., Alcı, M. and Altıntas, M., “Variable-time-interval trajectory optimization-based dynamic walking control of bipedal robot,” Robotica 40(6), 17991819 (2022).CrossRefGoogle 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
Kim, N., Jeong, B. and Park, K., “A novel methodology to explore the periodic gait of a biped walker under uncertainty using a machine learning algorithm,” Robotica 40(1), 120135 (2022).CrossRefGoogle Scholar
Kumar, J. and Dutta, A., “Energy optimal motion planning of a 14-DOF biped robot on 3D terrain using a new speed function incorporating biped dynamics and terrain geometry,” Robotica 40(2), 250278 (2022).CrossRefGoogle Scholar
Yao, C., Liu, C., Xia, L., Liu, M. and Chen, Q., “Humanoid adaptive locomotion control through a bioinspired CPG-based controller,” Robotica 40(3), 762779 (2022).CrossRefGoogle Scholar
Dip, G., Prahlad, V. and Kien, P. D., “Genetic algorithm-based optimal bipedal walking gait synthesis considering tradeoff between stability margin and speed,” Robotica 27(3), 355365 (2009).CrossRefGoogle Scholar
Tao, C., Xue, J., Zhang, Z., Cao, F., Li, C. and Gao, H., “Gait optimization method for humanoid robots based on parallel comprehensive learning particle swarm optimizer algorithm,” Front. Neurorobot. 14, 600885 (2021). doi: 10.3389/fnbot.2020.600885.CrossRefGoogle ScholarPubMed
Thien, H. T., Van Kien, C. and Anh, H. P. H., “Optimized stable gait planning of biped robot using multi-objective evolutionary JAYA algorithm,” Int. J. Adv. Robot. Syst. 17(6), 113 (2020).Google Scholar
Elhosseini, M. A., Haikal, A. Y., Badawy, M. and Khashan, N., “Biped robot stability based on an A-C parametric Whale Optimization Algorithm,” J. Comput. Sci. 31, 1732 (2019).CrossRefGoogle Scholar
Kuindersma, S., Deits, R., Fallon, M., Valenzuela, Aés, Dai, H., Permenter, F., Koolen, T., Marion, P. and Tedrake, R., “Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot,” Auton. Robots 40(3), 429455 (2016).CrossRefGoogle Scholar
Yang, S., Chen, H., Fu, Z. and Zhang, W., “Force-Feedback Based Whole-Body Stabilizer for Position-Controlled Humanoid Robots,” In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2021) pp. 74327439.Google Scholar
Ramuzat, N., Buondonno, G., Boria, S. and Stasse, O., “Comparison of Position and Torque Whole-Body Control Schemes on the Humanoid Robot TALOS,” In: 2021 20th International Conference on Advanced Robotics (ICAR) (2021) pp. 785792.Google Scholar
Gibson, G., Dosunmu-Ogunbi, O., Gong, Y. and Grizzle, J., “ALIP-Based Bipedal Locomotion Controller via Model Predictive Control and Virtual Constraints,” In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2022) pp. 67246731.Google Scholar
Daneshmand, E., Khadiv, M., Grimminger, F. and Righetti, L., “Variable horizon MPC with swing foot dynamics for bipedal walking control,” IEEE Robot. Autom. Lett. 6(2), 23492356 (2021).CrossRefGoogle Scholar
Habib, A. S., Smaldone, F. M., Scianca, N., Lanari, L. and Oriolo, G., “Handling Non-Convex Constraints in MPC-Based Humanoid Gait Generation,” In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2022) pp. 1316713173.Google Scholar
Guo, Y., Zhang, M., Dong, H. and Zhao, M., “Fast online planning for bipedal locomotion via centroidal model predictive gait synthesis,” IEEE Robot. Autom. Lett. 6(4), 64506457 (2021).CrossRefGoogle Scholar
Saidouni, T. and Bessonnet, G., “Generating globally optimised sagittal gait cycles of a biped robot,” Robotica 21(2), 199210 (2003).CrossRefGoogle Scholar
Bessonnet, G., Marot, J., Seguin, P. and Sardain, P., “Parametric-based dynamic synthesis of 3D-gait,” Robotica 28(4), 563581 (2010).CrossRefGoogle Scholar
Wang, J., Whitman, E. C. and Stilman, M., “Whole-Body Trajectory Optimization for Humanoid Falling,” In: Proceedings of the American Control Conference (2012).Google Scholar
Xiang, Y., Chung, H-J., Kim, J. H., Bhatt, R., Rahmatalla, S., Yang, J., Marler, T., Arora, J. S. and Abdel-Malek, K., “Predictive dynamics: An optimization-based novel approach for human motion simulation,” Struct. Multidiscip. Optim. 41(3), 465479 (2010).CrossRefGoogle Scholar
Shin, H.-K. and Kim, B. K., “Energy-efficient gait planning and control for biped robots utilizing the allowable ZMP region,” IEEE Trans. Robot. 30(4), 986993 (2014).CrossRefGoogle Scholar
Ding, J., Zhou, J., Guo, Z. and Xiao, X., “Energy-efficient bipedal walking: From single-mass model to three-mass model,” Robotica 39(9), 15371559 (2021).CrossRefGoogle Scholar
Vukobratovic, M., Borovac, B. and Potkonjak, V., “ZMP: A review of some basic misunderstandings,” Int. J. Humanoid Robot. 3(2), 153175 (2006).CrossRefGoogle Scholar
Wang, S., Mesesan, G., Englsberger, J., Lee, D. and Ott, C., “Online Virtual Repellent Point Adaptation for Biped Walking using Iterative Learning Control,” In: 2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids) (2021) pp. 112119.Google Scholar
Tazaki, Y., “Footstep and Timing Adaptation for Humanoid Robots Utilizing Pre-computation of Capture Regions,” In: 2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids) (2021) pp. 178184.Google Scholar
Chignoli, M., Kim, D., Stanger-Jones, E. and Kim, S., “The MIT Humanoid Robot: Design, Motion Planning, and Control for Acrobatic Behaviors,” In: 2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids) (2021) pp. 18.Google Scholar
Garcia, G., Griffin, R. and Pratt, J., “MPC-Based Locomotion Control of Bipedal Robots with Line-Feet Contact Using Centroidal Dynamics,” In: 2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids) (2021) pp. 276282.Google Scholar
Dafarra, S., Bertrand, S., Griffin, R. J., Metta, G., Pucci, D. and Pratt, J., “Non-Linear Trajectory Optimization for Large Step-Ups: Application to the Humanoid Robot Atlas,” In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2020) pp. 38843891.Google Scholar
Chevallereau, C., Grizzle, J. W. and Shih, C.-L., “Asymptotically stable walking of a five-link underactuated 3-D bipedal robot,” IEEE Trans. Robot. 25(1), 3750 (2009).CrossRefGoogle Scholar
Ramezani, A., Hurst, J. W., Hamed, K. A. and Grizzle, J. W., “Performance analysis and feedback control of ATRIAS, a three-dimensional bipedal robot,” J. Dyn. Syst. Meas. Control 136(2), 021012 (2014).CrossRefGoogle Scholar
Gong, Y., Hartley, R., Da, X., Hereid, A., Harib, O., Huang, J.-K. and Grizzle, J., “Feedback Control of a Cassie Bipedal Robot: Walking, Standing, and Riding a Segway,” In: 2019 American Control Conference (ACC) (2019) pp. 45594566.Google Scholar
Kakaei, M. M. and Salarieh, H., “New robust control method applied to the locomotion of a 5-link biped robot,” Robotica 38(11), 20232038 (2020).CrossRefGoogle Scholar
Erez, T., Tassa, Y. and Todorov, E., “Simulation Tools for Model-Based Robotics: Comparison of Bullet, Havok, MuJoCo, ODE and PhysX,” In: 2015 IEEE International Conference on Robotics and Automation (ICRA) (2015) pp. 43974404.Google Scholar
Griffin, B., Nonholonomic Virtual Constraints and Gait Optimization for Robust Robot Walking Control (University of Michigan, 2016).Google Scholar
Westervelt, E. R., Toward a Coherent Framework for the Control of Planar Biped Locomotion (University of Michigan, 2003).Google Scholar
Bijalwan, V., Semwal, V. B., Singh, G. and Mandal, T. K., “HDL-PSR: Modelling spatio-temporal features using hybrid deep learning approach for post-stroke rehabilitation,” Neural Process. Lett. 54(3), 279298 (2022).Google Scholar
Raj, M., Semwal, V. B. and Nandi, G. C., “Bidirectional association of joint angle trajectories for humanoid locomotion: The restricted Boltzmann machine approach,” Neural Comput. Appl. 30(6), 17471755 (2018).CrossRefGoogle Scholar
Hereid, A., Dynamic Humanoid Locomotion: Hybrid Zero Dynamics Based Gait Optimization via Direct Collocation Methods (Georgia Institute of Technology, Atlanta, Georgia, 2016).Google Scholar
Grizzle, J. W., Chevallereau, C., Sinnet, R. W. and Ames, A. D., “Models, feedback control, and open problems of 3D bipedal robotic walking,” Automatica 50(8), 19551988 (2014).CrossRefGoogle Scholar
Benson, D. A., A Gauss Pseudospectral Transcription for Optimal Control (University of Colorado, 2005).Google Scholar
Ren, H., Zhang, L. and Su, C., “Design and research of a walking robot with two parallel mechanisms,” Robotica 39(9), 16341641 (2021).CrossRefGoogle Scholar
Jain, R., Semwal, V. B. and Kaushik, P., “Stride segmentation of inertial sensor data using statistical methods for different walking activities,” Robotica 40(8), 25672580 (2022).CrossRefGoogle Scholar
Semwal, V. B., Gaud, N., Lalwani, P., Bijalwan, V. and Alok, A. K., “Pattern identification of different human joints for different human walking styles using inertial measurement unit (IMU) sensor,” Artif. Intell. Rev. 55(2), 11491169 (2022).CrossRefGoogle Scholar
Challa, S. K., Kumar, A., Semwal, V. B. and Dua, N., “An optimized-LSTM and RGB-D sensor-based human gait trajectory generator for bipedal robot walking,” IEEE Sens. J. 22(24), 2435224363 (2022).CrossRefGoogle Scholar
Hurmuzlu, Y. and Marghitu, D. B., “Rigid body collisions of planar kinematic chains with multiple contact points,” Int. J. Rob. Res. 13(1), 8292 (1994).CrossRefGoogle Scholar