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Linear Quadratic Regulator Method in Vision-Based Laser Beam Tracking for a Mobile Target Robot

Published online by Cambridge University Press:  03 July 2020

Yun Ling Open the ORCID record for Yun Ling [Opens in a new window] ,
Jian Wu ,
Weiping Zhou ,
Yubiao Wang  and
Changcheng Wu
Yun Ling*
Affiliation:
Nanjing Research Institute of Simulation Technology, Department of Research Center, Nanjing, Jiangsu
Jian Wu
Affiliation:
Nanjing Research Institute of Simulation Technology, Department of Research Center, Nanjing, Jiangsu
Weiping Zhou
Affiliation:
Nanjing Research Institute of Simulation Technology, Department of Combat Training, Nanjing, Jiangsu
Yubiao Wang
Affiliation:
Nanjing Research Institute of Simulation Technology, Department of Research Center, Nanjing, Jiangsu
Changcheng Wu
Affiliation:
Nanjing University of Aeronautics and Astronautics, College of Automation Engineering, Nanjing, Jiangsu
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper proposes a novel laser beam tracking mechanism for a mobile target robot that is used in shooting ranges. Compared with other traditional tracking mechanisms and modules, the proposed laser beam tracking mechanism is more flexible and low cost in use. The mechanical design and the working principle of the tracking module are illustrated, and the complete control system of the mobile target robot is introduced in detail. The tracking control includes two main steps: localizing the mobile target robot with regards to the position of the laser beam and tracking the laser beam by the linear quadratic regulator (LQR). First of all, the state function of the control system is built for this tracking system; second, the control law is deduced according to the discretized state function; lastly, the stability of the control method is proved by the Lyapunov theory. The experimental results demonstrate that the Hue, Saturation, Value feature-extracting method is robust and is qualified to be used for localization in the laser beam tracking control. It is verified through experiments that the LQR method is of better performance than the conventional Proportional Derivative control in the aspect of converge time, lateral error control, and distance error control.

Keywords

Laser beamPath trackingIndoor localizationLQRTarget robot
Type
Articles
Information
Robotica , Volume 39 , Issue 3 , March 2021 , pp. 524 - 534
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

National research council of the national academies, Technology Development for Army Unmanned Ground Vehicles (National Defense Industry Press, Beijing, 2009) pp.1–30.Google Scholar
Taponecco, L., D’Amico, A. A. and Mengali, U., “Joint TOA and AOA estimation for UWB localization applications,” IEEE Trans. Wireless Commun. 10(7), 2207–2217 (2011).CrossRefGoogle Scholar
Cicirelli, G., Milella, A. and Paola, D. D., “RFID tag localization by using adaptive neuro-fuzzy inference for mobile robot applications,” Ind. Robot Int. J. 39(4), 340–348 (2012).CrossRefGoogle Scholar
Zhang, L., Zapata, R. and Lepinay, P., “Self-adaptive Monte Carlo localization for mobile robots using range finders,” Robotica, Cambridge University Press. 30, 229–244 (2012).Google Scholar
Park, J., Hwang, W. S., Jwon, G. and Kim, K., “A novel line of sight control system for a robot vision tracking system, using vision feedback and motion-disturbance feedforward compensation,” Robotica 31, 99–112 (2012).CrossRefGoogle Scholar
Lyu, Y. J., Xu, A. L., Chang, C. C. and Cao, M. Y., “Application analysis of the linear CCD in the path recognition system,” Proceedings of the 2007 IEEE International Conference on Integration Technology, Shen Zhen, Guangdong, China (Mar, 20–24, 2007) pp. 151–154.Google Scholar
Kim, H. S., Seo, W. and Baek, K., “Indoor positioning system using magnetic field map navigation and an encoder system,” Sensors 17(3), 651–667 (2017).CrossRefGoogle Scholar
Thrun, S., Mentemerlo, M. and Dahlkamp, H., “Stanley: The robot that won the DARPA challenge,” J. Field Robot. 23(9), 661–692 (2006).CrossRefGoogle Scholar
Meng, X. P., Kou, L. and Yuan, Q., “Motion control of robot based on a new integral separated PID,” Int. J. Wireless Mob. Comput. 11(3), 207–213 (2016).CrossRefGoogle Scholar
Alouache, A. and Wu, Q. H., “Fuzzy logic PD controller for trajectory tracking of an autonomous differential drive mobile robot,” Ind. Robot Int. J. 45(1), 23–33 (2018).CrossRefGoogle Scholar
Sebastian, B. and Pinhas, B. T., “Active disturbance rejection control for handling slip in tracked vehicle locomotion,” J. Mech. Robot. 11(2), 389–399 (2019).CrossRefGoogle Scholar
Pacheco, L. and Luo, N., “Testing PID and MPC performance for mobile robot local path-following,” Int. J. Adv. Robot. Syst. 12(11), 155–171 (2015).CrossRefGoogle Scholar
Zhang, Z., Wang, L. and Li, L., “Design and implementation of two-wheeled mobile robot by variable structure sliding mode control,” Proceedings of the 35th Chinese Control Conference, Chengdu, Sichuan, China (Jul, 29–29, 2016) pp. 5869–5873.CrossRefGoogle Scholar
Jiang, Z. P. and Nijmeijer, H., “Tracking control of mobile robots: A case study in backstepping,” Automatica 33(7), 1393–1399 (1997).Google Scholar
Zhang, H. J., Gong, J. W. and Jiang, Y., “An iterative linear quadratic regulator based trajectory tracking controller for wheeled mobile robot,” J. Zhejiang Univ. 13(8), 593–600 (2012).CrossRefGoogle Scholar
Fox, D., Burgard, W. and Thrun, S., “The dynamic window approach to collision avoidance,” IEEE Robot. Autom. Mag. 4(1), 23–33 (1997).CrossRefGoogle Scholar
Velagic, J., Osmic, N. and Lacevic, B., “Neural network controller for mobile robot motion control,” Word Acad. Sci. Eng. Technol. Int. J. Electric. Comput. Eng. 2(11), 134–145 (2008).Google Scholar
Luy, N. T., Thanh, N. T. and Tri, H. M., “Reinforcement learning-based intelligent tracking control for wheeled mobile robot,” Trans. Institute Meas. Control 36(7), 868–877 (2014).CrossRefGoogle Scholar
Gonzalez, R. C. and Woods, R. E., Digital Image Processing (3rd edition) (Publishing House of Electronics Industry, Beijing, 2017) pp. 249–284.Google Scholar
Zhang, X. Y., Wang, X. Q. and Bai, F. Z., “A gray centroid method based light band center recognition algorithm,” Laser Infrared 46(5), 622–626 (2016).Google Scholar