Implementasi Sistem Kendali Keseimbangan Statis Pada Robot Quadruped Menggunakan Reinforcement Learning

https://doi.org/10.22146/ijeis.73865

Hidayat Eko Saputro(1*), Nur Achmad Sulistyo Putro(2), Sri Hartati(3), Ilona Usuman(4)

(1) Program Studi Elektronika dan Instrumentasi, FMIPA UGM, Yogyakarta, Indonesia
(2) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta, Indonesia
(3) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta, Indonesia
(4) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta, Indonesia
(*) Corresponding Author

Abstract


The basic thing to consider when building a quadruped robot is the issue of balance. These factors greatly determine the success of the quadruped robot in carrying out movements such as stabilizing the body on an inclined plane, walking movements and others. Conventional feedback control methods by performing mathematical modeling can be used to balance the robot. However, this method still has weaknesses. The application of conventional feedback control methods often results in an inaccurate controller, so it must be manually tuned for its application. In this study, reinforcement learning methods were used using Q-Learning algorithms. The use of reinforcement learning methods was chosen because no mathematical calculations are needed to control the balance of quadruped robots. The process of learning the system to train the agent's abilities is carried out using a Gazebo simulator. The learning results show that the system could run well as evidenced by the higher value of sum rewards per episode.


Keywords


Quadruped; Reinforcement Learning; Q-Learning; Rewards; Gazebo

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References

[1] T. Johannink et al., “Residual Reinforcement Learning for Robot Control,” 2019 International Conference on Robotics and Automation (ICRA), pp. 6023–6029, Dec. 2018, [Online]. Available: http://arxiv.org/abs/1812.03201

[2] R. C. Prayogo, A. Triwiyatno, and Sumardi, “Quadruped Robot with Stabilization Algorithm on Uneven Floor using 6 DOF IMU based Inverse Kinematic,” 2018 5th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), pp. 39–44, 2018.

[3] Z. Nasution, A. F. I. Suparman, G. A. Prasetyo, A. H. Alasiry, E. H. Binugroho, and A. Darmawan, “Body Balancing Control for EILERO Quadruped Robot while Walking on Slope,” 2019 International Electronics Symposium (IES), pp. 364–369, 2019.

[4] C. Yu, L. Zhou, H. Qian, and Y. Xu, “Posture Correction of Quadruped Robot for Adaptive Slope Walking,” Proceedings of the 2018 IEEE International Conference on Robotics and Biomimetics, Kuala Lumpur, Malaysia, pp. 1220–1225, Dec. 2018.

[5] A. T. B. Antok et al., “Quadruped Robot Balance Control for Stair Climbing Based on Fuzzy Logic,” in International Electronics Symposium 2021: Wireless Technologies and Intelligent Systems for Better Human Lives, IES 2021 - Proceedings, Sep. 2021, pp. 552– 557. doi: 10.1109/IES53407.2021.9594046.

[6] L. Cui et al., “Learning-Based Balance Control of Wheel-Legged Robots,” IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7667–7674, Oct. 2021, doi: 10.1109/LRA.2021.3100269.

[7] E. Li et al., “Model Learning for Two-Wheeled Robot Self-Balance Control,” Proceeding of the IEEE International Conference on Robotics and Biomimetics Dali, China, December 2019, pp. 1582–1587, 2019.

[8] L. Guo, S. A. A. Rizvi, and Z. Lin, “Optimal Control of a Two-Wheeled Self-Balancing Robot by Reinforcement Q-learning,” in IEEE International Conference on Control and Automation, ICCA, Oct. 2020, vol. 2020-October, pp. 955–960. doi: 10.1109/ICCA51439.2020.9264485.

[9] Y. Zhou, J. Lin, S. Wang, and C. Zhang, “Learning Ball-Balancing Robot through Deep Reinforcement Learning,” in 2021 International Conference on Computer, Control and Robotics, ICCCR 2021, Jan. 2021, pp. 1–8. doi: 10.1109/ICCCR49711.2021.9349369.

[10] B. Qin, Y. Gao, and Y. Bai, “Sim-to-real: Six-legged Robot Control with Deep Reinforcement Learning and Curriculum Learning,” in 2019 4th International Conference on Robotics and Automation Engineering, ICRAE 2019, Nov. 2019, pp. 1–5. doi: 10.1109/ICRAE48301.2019.9043822.



DOI: https://doi.org/10.22146/ijeis.73865

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