Bioinspired and Energy-Efficient Convex Model
Russian journal of nonlinear dynamics, Tome 18 (2022) no. 5, pp. 831-841.

Voir la notice de l'article provenant de la source Math-Net.Ru

Animal running has been studied for a long time, but until now robots cannot repeat the same movements with energy efficiency close to animals. There are many controllers for con- trolling the movement of four-legged robots. One of the most popular is the convex MPC. This paper presents a bioinspirational approach to increasing the energy efficiency of the state-of-the- art convex MPC controller. This approach is to set a reference trajectory for the convex MPC in the form of an SLIP model, which describes the movements of animals when running. Adding an SLIP trajectory increases the energy efficiency of the Pronk gait by 15 percent over a range of speed from 0.75 m/s to 1.75 m/s.
Keywords: quadruped, model predictive control, spring-loaded inverted pendulum, energy efficiency.
Mots-clés : bioinspiration
@article{ND_2022_18_5_a6,
     author = {A. D. Shamraev and S. A. Kolyubin},
     title = {Bioinspired and {Energy-Efficient} {Convex} {Model}},
     journal = {Russian journal of nonlinear dynamics},
     pages = {831--841},
     publisher = {mathdoc},
     volume = {18},
     number = {5},
     year = {2022},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/ND_2022_18_5_a6/}
}
TY  - JOUR
AU  - A. D. Shamraev
AU  - S. A. Kolyubin
TI  - Bioinspired and Energy-Efficient Convex Model
JO  - Russian journal of nonlinear dynamics
PY  - 2022
SP  - 831
EP  - 841
VL  - 18
IS  - 5
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/ND_2022_18_5_a6/
LA  - en
ID  - ND_2022_18_5_a6
ER  - 
%0 Journal Article
%A A. D. Shamraev
%A S. A. Kolyubin
%T Bioinspired and Energy-Efficient Convex Model
%J Russian journal of nonlinear dynamics
%D 2022
%P 831-841
%V 18
%N 5
%I mathdoc
%U http://geodesic.mathdoc.fr/item/ND_2022_18_5_a6/
%G en
%F ND_2022_18_5_a6
A. D. Shamraev; S. A. Kolyubin. Bioinspired and Energy-Efficient Convex Model. Russian journal of nonlinear dynamics, Tome 18 (2022) no. 5, pp. 831-841. http://geodesic.mathdoc.fr/item/ND_2022_18_5_a6/

[1] Raibert, M. H., Legged Robots That Balance, MIT Press, Cambridge, Mass., 1986, 205 pp.

[2] Di Carlo, J., Software and Control Design for the MIT Cheetah Quadruped Robots, Master's Thesis, Massachusetts Institute of Technology, Cambridge, Mass., 2020, 100 pp.

[3] Kim, D., Lee, J., Ahn, J., Campbell, O., Hwang, H., and Sentis, L., “Computationally-Robust and Efficient Prioritized Whole-Body Controller with Contact Constraints”, IEEE/RSJ Internat. Conf. on Intelligent Robots and Systems (IROS, Madrid, Spain, Oct 2018), 5987–5994

[4] Di Carlo, J., Wensing, P. M., Katz, B., Bledt, G., and Kim, S., “Dynamic Locomotion in the MIT Cheetah 3 through Convex Model-Predictive Control”, IEEE/RSJ Internat. Conf. on Intelligent Robots and Systems (IROS, Madrid, Spain, Oct 2018), 7440–7447

[5] Bledt, G., Wensing, P. M., and Kim, S., “Policy-Regularized Model Predictive Control to Stabilize Diverse Quadrupedal Gaits for the MIT Cheetah”, IEEE/RSJ Internat. Conf. on Intelligent Robots and Systems (IROS, Vancouver, Canada, Sep 2017), 4102–4109

[6] Bledt, G. and Kim, S., “Implementing Regularized Predictive Control for Simultaneous Real-Time Footstep and Ground Reaction Force Optimization”, IEEE/RSJ Internat. Conf. on Intelligent Robots and Systems (IROS, Macau, China, Nov 2019), 6316–6323

[7] Bledt, G. and Kim, S., “Extracting Legged Locomotion Heuristics with Regularized Predictive Control”, 2020 IEEE International Conf. on Robotics and Automation (ICRA, Paris, France, May–Aug 2020), 406–412

[8] Bledt, G., Regularized Predictive Control Framework for Robust Dynamic Legged Locomotion, PhD Thesis, Massachusetts Institute of Technology, Cambridge, Mass., 2020, 160 pp.

[9] Kim, D., Di Carlo, J., Katz, B., Bledt, G., and Kim, S., Highly Dynamic Quadruped Locomotion via Whole-Body Impulse Control and Model Predictive Control, 2019, 8 pp., arXiv: 1909.06586 [cs.RO]

[10] Dudzik, T., Chignoli, M., Bledt, G., and Kim, S., “Robust Autonomous Navigation of a Small-Scale Quadruped Robot in Real-World Environments”, 2020 IEEE/RSJ Internat. Conf. on Intelligent Robots and Systems (IROS, Las Vegas, Nev., Oct 2020), 3664–3671

[11] Kim, D., Carballo, D., Di Carlo, J., Katz, B., Bledt, G., Lim, B., and Kim, S., “Vision Aided Dynamic Exploration of Unstructured Terrain with a Small-Scale Quadruped Robot”, 2020 IEEE International Conf. on Robotics and Automation (ICRA, Paris, France, May–Aug 2020), 2464–2470

[12] Margolis, G. B., Chen, T., Paigwar, K., Fu, X., Kim, D., Kim, S., and Agrawal, P., “Learning to Jump from Pixels”, Proc. Mach. Learn. Res., 164 (2022), 1025–1034

[13] Boussema, C., Powell, M. J., Bledt, G., Ijspeert, A. J., Wensing, P. M., and Kim, S., “Online Gait Transitions and Disturbance Recovery for Legged Robots via the Feasible Impulse Set”, IEEE Robot. Autom. Lett., 4:2 (2019), 1611–1618

[14] Carius, J., Farshidian, F., and Hutter, M., “MPC-Net: A First Principles Guided Policy Search”, IEEE Robot. Autom. Lett., 5:2 (2020), 2897–2904

[15] Peng, X. B., Coumans, E., Zhang, T., Lee, T.-W. E., Tan, J., and Levine, S., Learning Agile Robotic Locomotion Skills by Imitating Animals, 2020, arXiv: 2004.00784 [cs.RO]

[16] Green, K., Godse, Y., Dao, J., Hatton, R. L., Fern, A., and Hurst, J., “Learning Spring Mass Locomotion: Guiding Policies with a Reduced-Order Model”, IEEE Robot. Autom. Lett., 6:2 (2021), 3926–3932

[17] Li, H., Frei, R. J., and Wensing, P. M., “Model Hierarchy Predictive Control of Robotic Systems”, IEEE Robot. Autom. Lett., 6:2 (2021), 3373–3380

[18] Craig, J. J., Introduction to Robotics: Mechanics and Control, 3rd ed, Pearson, New York, 2004, 408 pp.

[19] Bledt, G., Powell, M. J., Katz, B., Di Carlo, J., Wensing, P. M., and Kim, S., “MIT Cheetah 3: Design and Control of a Robust, Dynamic Quadruped Robot”, Proc. of the 2018 IEEE/RSJ Internat. Conf. on Intelligent Robots and Systems (IROS, Madrid, Spain, Oct 2018), 2245–2252 \itemsep=2pt

[20] Geyer, H., Seyfarth, A., and Blickhan, R., “Compliant Leg Behaviour Explains Basic Dynamics of Walking and Running”, Proc. R. Soc. Lond. Ser. B Biol. Sci., 273:1603 (2006), 2861–2867

[21] Kuo, A. D., “The Relative Roles of Feedforward and Feedback in the Control of Rhythmic Movements”, Motor Control, 6:2 (2002), 129–145

[22] Ryczko, D., Simon, A., and Ijspeert, A. J., “Walking with Salamanders: From Molecules to Biorobotics”, Trends Neurosci., 43:11 (2020), 916–930

[23] Grandia, R., Jenelten, F., Yang, Sh., Farshidian, F., and Hutter, M., Perceptive Locomotion through Nonlinear Model Predictive Control, 2022, 20 pp., arXiv: 2208.08373 [cs.RO]