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A novel methodology to explore the periodic gait of a biped walker under uncertainty using a machine learning algorithm

Published online by Cambridge University Press:  28 May 2021

Namjung Kim ,
Bongwon Jeong Open the ORCID record for Bongwon Jeong [Opens in a new window]  and
Kiwon Park
Namjung Kim
Affiliation:
Department of Mechanical Engineering, Gachon University, Seongnam, South Korea
Bongwon Jeong
Affiliation:
Innovative SMR System Development Division, Korea Atomic Energy Research Institute, Daejeon, South Korea
Kiwon Park*
Affiliation:
Department of Mechatronics Engineering, Incheon National University, Incheon, South Korea
*
*Corresponding author. Email: [email protected]
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Abstract

In this paper, we present a systematic approach to improve the understanding of stability and robustness of stability against the external disturbances of a passive biped walker. First, a multi-objective, multi-modal particle swarm optimization (MOMM-PSO) algorithm was employed to suggest the appropriate initial conditions for a given biped walker model to be stable. The MOMM-PSO with ring topology and special crowding distance (SCD) used in this study can find multiple local minima under multiple objective functions by limiting each agent’s search area properly without determining a large number of parameters. Second, the robustness of stability under external disturbances was studied, considering an impact in the angular displacement sampled from the probabilistic distribution. The proposed systematic approach based on MOMM-PSO can find multiple initial conditions that lead the biped walker in the periodic gait, which could not be found by heuristic approaches in previous literature. In addition, the results from the proposed study showed that the robustness of stability might change depending on the location on a limit cycle where immediate angular displacement perturbation occurs. The observations of this study imply that the symmetry of the stable region about the limit cycle will break depending on the accelerating direction of inertia. We believe that the systematic approach developed in this study significantly increased the efficiency of finding the appropriate initial conditions of a given biped walker and the understanding of robustness of stability under the unexpected external disturbance. Furthermore, a novel methodology proposed for biped walkers in the present study may expand our understanding of human locomotion, which in turn may suggest clinical strategies for gait rehabilitation and help develop gait rehabilitation robotics.

Keywords

bipedslegged robotsuncertainty quantificationstability analysishuman biomechanics
Type
Article
Information
Robotica , Volume 40 , Issue 1 , January 2022 , pp. 120 - 135
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

McGeer, T., “Passive dynamic walking,” Int. J. Rob. Res. 9(2), 62–82 (1990).CrossRefGoogle Scholar
Collins, S. H., Wisse, M. and Ruina, A., “A three-dimensional passive-dynamic walking robot with two legs and knees,” Int. J. Rob. Res. 20(7), 607–615 (2001).CrossRefGoogle Scholar
Goswami, A., Thuilot, B. and Espiau, B., “Compass-like biped robot part I: Stability and bifurcation of passive gaits,” (INRIA: Technical Report, 1996).Google Scholar
Sakagami, Y., Watanabe, R., Aoyama, C., Matsunaga, S., Higaki, N. and Fujimura, K., “The Intelligent ASIMO: System Overview and Integration,” IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3 (IEEE, 2002) pp. 2478–2483.Google Scholar
Kim, J. Y., Lee, J. and Ho, J. H., “Experimental realization of dynamic walking for a human-riding biped robot, HUBO FX-1,” Adv. Rob. 21(3–4), 461–484 (2007).CrossRefGoogle Scholar
Kim, J. Y., Park, I. W. and Oh, J. H., “Experimental realization of dynamic walking of the biped humanoid robot KHR-2 using zero moment point feedback and inertial measurement,” Adv. Rob. 20(6), 707–736 (2006).Google Scholar
Vukobratoviæ, M. and Borovac, B. ,”Zero-moment point—thirty five years of its life,” Int. J. Hum. Rob. 1(1), 157–173 (2004).CrossRefGoogle Scholar
Hubicki, C., Energy-Economical Heuristically Based Control of Compass Gait Walking on Stochastically Varying Terrain Master’s thesis (Bucknell University, 2011).Google Scholar
Kennedy, J. and Eberhart, R., “Particle Swarm Optimization,” Proceedings of ICNN’95-International Conference on Neural Networks, vol. 4 (IEEE, 1995) pp. 1942–1948.CrossRefGoogle Scholar
Hürmüzlü, Y. and Moskowitz, G. D., “The role of impact in the stability of bipedal locomotion,” Dyn. Stab. Syst. 1(3), 217–234 (1986).Google Scholar
Anstensrud, T., 2-D Passive Compass Biped Walker: Analysis and Robustness of Stable Gait Master’s thesis (Institutt for tekniskky bernetikk, 2013).Google Scholar
Mitchell, M., An Introduction to Genetic Algorithms (MIT Press, Cambridge, MA, 1996).Google Scholar
Dorigo, M. and Di Caro, G., “Ant Colony Optimization: A New Meta-Heuristic,” Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), vol. 2 (IEEE, 1999) pp. 1470–1477.CrossRefGoogle Scholar
Reyes-Sierra, M. and Coello, C. C., “Multi-objective particle swarm optimizers: A survey of the state-of-the-art,” Int. J. Comput. Intell. Res. 2(3), 287–308 (2006).Google Scholar
Preuss, M., “Niching Methods and Multimodal Optimization Performance,” In: Multimodal Optimization by Means of Evolutionary Algorithms (Springer, Cham, (2015) pp. 115–137.CrossRefGoogle Scholar
Goldberg, D. E. and Richardson, J., “Genetic Algorithms with Sharing for Multimodal Function Optimization,” Genetic Algorithms and their Applications: Proceedings of the Second International Conference on Genetic Algorithms (Lawrence Erlbaum, Hillsdale, NJ, 1987) pp. 41–49.Google Scholar
Mengshoel, O. J. and Goldberg, D. E., “Probabilistic Crowding: Deterministic Crowding with Probabilistic Replacement,” Proceedings of the Genetic and Evolutionary Computation Conference, vol. 1 (Morgan Kauffman, 1999) pp. 409–416.Google Scholar
Pétrowski, A., “A Clearing Procedure as a Niching Method for Genetic Algorithms,” Proceedings of IEEE International Conference on Evolutionary Computation (IEEE, 1996) pp. 798–803.Google Scholar
Li, J. P., Balazs, M. E., Parks, G. T. and Clarkson, P. J., “A species conserving genetic algorithm for multimodal function optimization,” Evol. Comput. 10(3), 207–234 (2002).CrossRefGoogle ScholarPubMed
Yue, C., Qu, B. and Liang, J., “A multiobjective particle swarm optimizer using ring topology for solving multimodal multiobjective problems,” IEEE Trans. Evol. Comput. 22(5), 805–817 (2017).CrossRefGoogle Scholar
Coello, C. A. C., Pulido, G. T. and Lechuga, M. S., “Handling multiple objectives with particle swarm optimization,” IEEE Trans. Evol. Comput. 8(3), 256–279 (2004).CrossRefGoogle Scholar
Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T., “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput. 6(2), 182–197 (2002).CrossRefGoogle Scholar
Gallego, J. A., Forner-Cordero, A., Moreno, J. C., Montellano, A., Turowska, E. A. and Pons, J. L., “Continuous Assessment of Gait Stability in Limit Cycle Walkers,” 2010 3rd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (IEEE, 2010) pp. 734–739.Google Scholar