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Implementation Method of the Robot Adaptation to Contact Interaction Mode Changes Using Deep Fully Connected Neural Networks

https://doi.org/10.21869/2223-1560-2020-24-1-206-214

Abstract

Purpose of research. The present paper conserns the problem of using reaction predictors in the control system of bipedal walking robots. The main advantage of using predictors is the ability to exclude unknown reaction forces from the dynamics equations and, consequently, from the robot control problem statements based on the model. An additional advantage of predictor setting of control tasks is also discussed in the paper, namely the possibility of its use to predict changes in contact interaction modes, such as slipping motion or foot lifting from the supporting surface.

Methods. The following methods are used in the research: the method of dynamics of multi-mass systems is necessary for developing a mathematical model of the behavior of a walking robot and describing its contact interaction with the support surface, the method of neural networks is used to develop a predictor that allows one to forecast the values of reactions between the robot’s foot and the surface.

Results. The paper shows that there is a connection between the frequencies of the harmonic components of robot movements (the ratio p of these frequencies in the experiment and the training sample) and the quality of reactions predictor operation of the support surface. This indicates the importance of applying a representative spectrum of walking robot movement frequencies in forming a training sample, and the poor generalizability of the predictor in relation to movement frequency.

Conclusion. The paper has considered the use of a reaction predictor to identify the possibility of changing the mode of contact interaction, based on the measurement of discrepancies between local linearizations for various discrete steps. The results obtained in this work will be used in the development of a motion control system for a bipedal walking robot, which allows the device to adapt to the parameters of the support surface on which the movement occurs.

About the Authors

S. I. Savin
Innopolis University
Russian Federation

Sergey I. Savin, Cand. of Sci. (Engineering), Higher Senior Officer, Laboratory of Mechatronics, Management and Prototyping

Universitetskaya str. 1, Innopolis 420500



L. Yu. Vorochaeva
Southwest State University
Russian Federation

Lyudmila Yu. Vorochaeva, Cand. of Sci. (Engineering), Associate Professor, Department of Mechanics, Mechatronics and Robotics

50 Let Oktyabrya str. 94, Kursk 305040



A. V. Malchikov
Southwest State University
Russian Federation

Аndrey V. Malchikov, Cand. of Sci. (Engineering), Associate Professor, Department of Mechanics, Mechatronics and Robotics

50 Let Oktyabrya str. 94, Kursk 305040



A. M. Salikhzyanov
Innopolis University
Russian Federation

Аlek М. Salikhzyanov, Student

Universitetskaya str. 1, Innopolis 420500



E. M. Zalyaev
Innopolis University
Russian Federation

Eduard М. Zalyaev, Student

Universitetskaya str. 1, Innopolis 420500



References

1. Radford N.A., Strawser P., Hambuchen K., Mehling J. S., Verdeyen W. K., Donnan A. S., ... Berka R. Valkyrie: Nasa's first bipedal humanoid robot. J. of Field Robotics, 2015, vol. 32(3), pp. 397-419.

2. Savin S., Jatsun S., Vorochaeva L. Trajectory generation for a walking in-pipe robot moving through spatially curved pipes. MATEC Web of Conf. EDP Sciences, 2017, vol. 113, pp. 1-5.

3. Akizono J., Iwasaki M., Nemoto T., Asakura O. Development on walking robot for underwater inspection. Advanced Robotics, 1989, pp. 652-663.

4. Vukobratović M., Borovac B. Zero-moment point—thirty five years of its life. Intern. J. of humanoid robotics, 2004, vol. 1(01), pp. 157-173.

5. Kajita S., Kanehiro F., Kaneko K., Fujiwara K., Harada K., Yokoi K., Hirukawa H. Biped walking pattern generation by using preview control of zero-moment point. Robotics and Automation: Proc. IEEE Intern. Conf., 2003, vol. 2, pp. 1620-1626.

6. Caron S., Pham Q.-C., Nakamura Y. Stability of surface contacts for humanoid robots: Closed-form formulae of the contact wrench cone for rectangular support areas. ICRA: Proc. IEEE Intern. Conf. on Robotics and Automation, Seattle, WA, USA, 2015, pp. 5107-5112.

7. Levine S., Wagener N., Abbeel P. Learning contact-rich manipulation skills with guided policy search. ICRA: Proc. IEEE Intern. Conf. on Robotics and Automation. Seattle, WA, USA, 2015. arXiv preprint arXiv:1501.05611.

8. Tan J., Zhang T., Coumans E., Iscen A., Bai Y., Hafner D., Vanhoucke V. Sim-to-real: Learning agile locomotion for quadruped robot. arXiv preprint arXiv:1804.10332. 2018.

9. Savin S., Khusainov R., Klimchik A. Admissible region ZMP trajectory generation for bipedal robots walking over uneven terrain. Zavalishin's Readings: Proc. 14th Intern. Conf. on Electromechanics and Robotics. Springer, Singapore, 2020, pp. 125-136.

10. Savin S. Neural Network-Based Reaction Estimator for Walking Robots. RusAutoCon: Proc. IEEE Intern. Russian Automation Conf. Sochi, Russia, 2018, pp. 1-6.

11. Aghili F. A unified approach for inverse and direct dynamics of constrained multibody systems based on linear projection operator: applications to control and simulation. IEEE Transactions on Robotics, 2005, vol. 21(5), pp. 834-849.

12. Laine F., Tomlin C. Efficient Computation of Feedback Control for Constrained Systems. arXiv preprint arXiv:1807.00794. 2018.


Review

For citations:


Savin S.I., Vorochaeva L.Yu., Malchikov A.V., Salikhzyanov A.M., Zalyaev E.M. Implementation Method of the Robot Adaptation to Contact Interaction Mode Changes Using Deep Fully Connected Neural Networks. Proceedings of the Southwest State University. 2020;24(1):206-214. (In Russ.) https://doi.org/10.21869/2223-1560-2020-24-1-206-214

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ISSN 2223-1560 (Print)
ISSN 2686-6757 (Online)