LQR-based control strategy for improving human-robot companionship and natural obstacle avoidance

Abstract

In the dynamic and unstructured environment of human-robot symbiosis, companion robots require natural human-robot interaction and autonomous intelligence through multimodal information fusion to achieve effective collaboration. Nevertheless, the control precision and coordination of the accompanying actions are not satisfactory in practical applications. This is primarily attributed to the difficulties in the motion coordination between the accompanying target and the mobile robot. This paper proposes a companion control strategy based on the Linear Quadratic Regulator (LQR) to enhance the coordination and precision of robot companion tasks. This method enables the robot to adapt to sudden changes in the companion target’s motion. Besides, the robot could smoothly avoid obstacles during the companion process. Firstly, a human-robot companion interaction model based on nonholonomic constraints is developed to determine the relative position and orientation between the robot and the companion target. Then, an LQR-based companion controller incorporating behavioral dynamics is introduced to simultaneously avoid obstacles and track the companion target’s direction and velocity. Finally, various simulations and real-world human-robot companion experiments are conducted to regulate the relative position, orientation, and velocity between the target object and the robot platform. Experimental results demonstrate the superiority of this approach over conventional control algorithms in terms of control distance and directional errors throughout system operation. The proposed LQR-based control strategy ensures coordinated and consistent motion with target persons in social companion scenarios.

Keywords

Human-robot interaction / Linear quadratic regulator / Human-accompanying robots / Behavioral dynamics

CRediT authorship contribution statement

Zefan Su: Writing - review & editing, Writing - original draft, Visualization, Validation, Software, Project administration, Methodology, Data curation, Conceptualization. Hanchen Yao: Writing - review & editing. Jianwei Peng: Conceptualization. Zhelin Liao: Data curation. Zengwei Wang: Data curation. Hui Yu: Writing - review & editing. Houde Dai: Writing - review & editing, Visualization, Validation. Tim C. Lueth: Writing - review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (61973293), the Fujian Provincial Science and Technology Plan Project (2023T3008, 2023T3069, and 2023T3084), the Quanzhou Science and Technology Plan Project (2022FX7), the Open Project Program of Fujian Key Laboratory of Special Intelligent Equipment Measurement and Control (FJIES2023KF02).

References

[1] S.S Honig, T. Oron-Gilad, H. Zaichyk, V. Sarne-Fleischmann, S. Olatunji, Y. Edan, Toward socially aware person-following robots, IEEE Trans. Cogn. Dev. Syst. 10 (4) (2018) 936-954.

[2] M.J. Islam, J. Hong, J. Sattar, Person-following by autonomous robots: A categorical overview, Int. J. Robot. Res. 38 (14) (2019) 1581-1618.

[3] Q.Y. Yan, J. Huang, Z.H. Yang, Y. Hasegawa, T. Fukuda, Human-following control of cane-type walking-aid robot within fixed relative posture, IEEE/ASME Trans. Mechatronics 27 (1) (2021) 537-548.

[4] S. Li, K. Milligan, P. Blythe, Y.H.Z. Zhang, S. Edwards, N. Palmarini, L. Corner, Y.J. Ji, F. Zhang, A. Namdeo, Exploring the role of human-following robots in supporting the mobility and wellbeing of older people, Sci. Rep. 13 (1)(2023) 6512.

[5] N.V. Toan, M.D. Hoang, P.B. Khoi, S.-Y. Yi, The human-following strategy for mobile robots in mixed environments, Robot. Auton. Syst. 160 (2023) 104317.

[6] Y. Sun, L. Sun, J.T. Liu, Human comfort following behavior for ser-vice robots, in: 2016 IEEE International Conference on Robotics and Biomimetics, ROBIO, IEEE, 2016, pp. 649-654.

[7] J.W. Peng, Z.L. Liao, Z.F. Su, H.C. Yao, Y.D. Zeng, H.D. Dai, Human-robot interaction dynamics-based impedance control strategy for enhancing social acceptance of human-following robot, in: 2023 China Automation Congress, CAC, IEEE, 2023, pp. 7354-7360.

[8] J. Yuan, S.M. Zhang, Q.X. Sun, G.D. Liu, J.X. Cai, Laser-based intersection-aware human following with a mobile robot in indoor environments, IEEE Trans. Syst. Man Cybern.: Syst. 51 (1) (2018) 354-369.

[9] M. Costa, Interpersonal distances in group walking, J. Nonverbal Behav. 34 (2010) 15-26.

[10] E. Repiso, A. Garrell, A. Sanfeliu, Adaptive side-by-side social robot naviga-tion to approach and interact with people, Int. J. Soc. Robot. 12 (4) (2020) 909-930.

[11] Y. Morales, S. Satake, R. Huq, D. Glas, T. Kanda, N. Hagita, How do people walk side-by-side? Using a computational model of human behavior for a social robot, in:Proceedings of the 7th Annual ACM/IEEE International Conference on Human-Robot Interaction, 2012, pp. 301-308.

[12] Y. Morales, T. Kanda, N. Hagita, Walking together: Side-by-side walking model for an interacting robot, J. Hum.-Robot Interact. 3 (2) (2014) 50-73.

[13] H.C. Yao, H.D. Dai, E.H. Zhao, P.H. Liu, R. Zhao, Laser-based side-by-side following for human-following robots, in: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, IEEE, 2021, pp. 2651-2656.

[14] G. Xue, H.C. Yao, Y.C. Zhang, J.Y. Huang, L.Q. Zhu, H.D. Dai, UWB-based adaptable side-by-side following for human-following robots, in: 2022 IEEE International Conference on Robotics and Biomimetics, ROBIO, IEEE, 2022, pp. 333-338.

[15] K. Morioka, J.-H. Lee, H. Hashimoto, Human-following mobile robot in a distributed intelligent sensor network, IEEE Trans. Ind. Electron. 51 (1)(2004) 229-237.

[16] J.-S. Hu, J.-J. Wang, D.M. Ho, Design of sensing system and anticipative behavior for human following of mobile robots, IEEE Trans. Ind. Electron. 61 (4) (2013) 1916-1927.

[17] H.Y. Tian, X. Ma, Behavioral dynamics-based impedance control for col-lision avoidance of human-following robots, in: 2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR, IEEE, 2022, pp. 349-354.

[18] E. Prassler, D. Bank, B. Kluge, Motion coordination between a human and a mobile robot, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 2, IEEE, 2002, pp. 1228-1233.

[19] O. Khatib, Real-time obstacle avoidance for manipulators and mobile robots, Int. J. Robot. Res. 5 (1) (1986) 90-98.

[20] G. Arechavaleta, J.-P. Laumond, H. Hicheur, A. Berthoz, On the non-holonomic nature of human locomotion, Auton. Robots 25 (2008) 25-35.

[21] D.D. Zhao, X.D. Yang, Y.C. Li, L. Xu, J.H. She, S. Yan, A Kalman-Koopman LQR control approach to robotic systems, IEEE Trans. Ind. Electron. (2024).

[22] S. Zoboli, V. Andrieu, D. Astolfi, G. Casadei, J.S Dibangoye, M. Nadri, Reinforcement learning policies with local LQR guarantees for nonlinear discrete-time systems, in: 2021 60th IEEE Conference on Decision and Control, CDC, IEEE, 2021, pp. 2258-2263.

[23] B.R. Fajen, W.H. Warren, Behavioral dynamics of steering, obstable avoid-ance, and route selection, J. Exp. Psychol.: Hum. Percept. Perform. 29 (2)(2003) 343.