Volume 19, Issue 7 (July 2019)                   Modares Mechanical Engineering 2019, 19(7): 1767-1777 | Back to browse issues page

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Khonsarian R, Farrokhi M. Vision-Based Model Predictive Control of Wheeled Mobile Robot. Modares Mechanical Engineering 2019; 19 (7) :1767-1777
URL: http://mme.modares.ac.ir/article-15-23019-en.html
1- Control Department, Electrical Engineering School, Iran University of Science and Technology, Tehran, Iran
2- Control Department, Electrical Engineering School, Iran University of Science and Technology, Tehran, Iran , farrokhi@iust.ac.ir
Abstract:   (3474 Views)

In this article, a novel control of wheeled mobile robot based on machine vision is considered. One of the common methods for controlling such systems is the use of Model Predictive Control (MPC) algorithms. In these systems, the response speed of the control algorithm and the optimality of these are two basic factors for achieving the optimal performance. Also, the impossibility of achieving precise values of the robot parameters and their variation during the operation of the robot is an important challenge in the implementation of the controller, therefore, this paper focuses on real-time and robust MPC, so that it can ensure the system against uncertainties and environmental disturbances in addition to the optimal and real-time response. Hence, the optimization based on projection recurrent neural network (PRNN) has been used as an optimizer to reduce the calculation time cost. The combination of PRNN optimization with MPC leads to new formulation and constraints that are considered to be the article innovations. Finally, in order to verify the validity of the proposed algorithm, the robot passes through the corridor with the presence of obstacles, which is simulated in the V-REP software. The results show that the optimum control input speed has been increased in comparison with similar methods, and the optimal path selection by the fuzzy system in the presence of obstacles has been well suited.

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Article Type: Original Research | Subject: Machining
Received: 2018/07/12 | Accepted: 2019/01/15 | Published: 2019/07/1

1. Martinez-Gomez J, Fernandez-Caballero A, Garcia-Varea I, Rodriguez L, Romero Gonzalez C. A taxonomy of vision systems for ground mobile robots. International Journal of Advanced Robotic Systems. 2014;11(7). [Link] [DOI:10.5772/58900]
2. Wang H, Guo D, Liang X, Chen W, Hu G, Leang KK. Adaptive vision-based leader-follower formation control of mobile robots. IEEE Transactions on Industrial Electronics. 2017;64(4):2893-2902. [Link] [DOI:10.1109/TIE.2016.2631514]
3. Zhang X, Fang Y, Li B, Wang J. Visual servoing of nonholonomic mobile robots with uncalibrated camera-to-robot parameters. IEEE Transactions on Industrial Electronics. 2017;64(1):390-400. [Link] [DOI:10.1109/TIE.2016.2598526]
4. Gupta M, Kumar S, Behera L, Subramanian VK. A novel vision-based tracking algorithm for a human-following mobile robot. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2017;47(7):1415-1427. [Link] [DOI:10.1109/TSMC.2016.2616343]
5. Li B, Zhang X, Fang Y, Shi W. Visual servo regulation of wheeled mobile robots with simultaneous depth identification. IEEE Transactions on Industrial Electronics. 2018;65(1):460-469. [Link] [DOI:10.1109/TIE.2017.2711861]
6. Harasim P, Trojnacki M. State of the Art in Predictive Control of Wheeled Mobile Robots. Journal of Automation Mobile Robotics and Intelligent Systems. 2016;10(1):34-42. [Link] [DOI:10.14313/JAMRIS_1-2016/5]
7. Li Z, Xiao H, Yang C, Zhao Y. Model predictive control of nonholonomic chained systems using general projection neural networks optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2015;45(10):1313-1321. [Link] [DOI:10.1109/TSMC.2015.2398833]
8. Azizi MR, Keighobadi J. Point stabilization of nonholonomic spherical mobile robot using nonlinear model predictive control. Robotics and Autonomous Systems. 2017;98:347-359. [Link] [DOI:10.1016/j.robot.2017.09.015]
9. Ke F, Li Z, Yang C. Robust tube-based predictive control for visual servoing of constrained differential-drive mobile robots. IEEE Transactions on Industrial Electronics. 2018;65(4):3437-3446. [Link] [DOI:10.1109/TIE.2017.2756595]
10. Li Z, Yang C, Su CY, Deng J, Zhang W. Vision-based model predictive control for steering of a nonholonomic mobile robot. IEEE Transaction on Control Systems and Technology. 2016;24(2):553-564. [Link]
11. Xiao H, Li Z, Yang C, Zhang L, Yuan P, Ding L, et al. Robust stabilization of a wheeled mobile robot using model predictive control based on neuro-dynamics optimization. IEEE Transactions on Industrial Electronics. 2017;64(1):505-516. [Link] [DOI:10.1109/TIE.2016.2606358]
12. Sarkar N, Yun X, Kumar V. Control of mechanical systems with rolling constraints: Application to dynamic control of mobile robots. The International Journal of Robotics Research. 1994;13(1):55-69. [Link] [DOI:10.1177/027836499401300104]
13. Zhang Y, Ge SS, Lee TH. A unified quadratic-programming-based dynamical system approach to joint torque optimization of physically constrained redundant manipulators. IEEE Transaction on System, Man and Cybernetics, Part B (Cybernetics). 2004;34(5):2126-2132. [Link] [DOI:10.1109/TSMCB.2004.830347]
14. Wang LX. A Course in Fuzzy Systems and Control. New Jercy: Prentice Hall PTR; 1997. [Link]
15. Chen J, Jia B, Zhang K. Trifocal, Tensor-based adaptive visual trajectory tracking control of mobile robots. IEEE Transactions on Cybernetics. 2017;47(11):3784-3798. [Link] [DOI:10.1109/TCYB.2016.2582210]
16. Agüero CE, Koenig N, Chen I, Boyer H, Peters S, Hsu J, et al. Inside the virtual robotics challenge: Simulating real-time robotic disaster response. IEEE Transactions on Automation Science and Engineering. 2015;12(2):494-506. [Link] [DOI:10.1109/TASE.2014.2368997]
17. MobileRobots Inc. Pioneer 3 operations manual with mobile robots exclusive advanced robot control & operations software. Version 3. [Internet]. 2006 [Cited 2018 July 01]. Available from: https://www.inf.ufrgs.br/~prestes/Courses/Robotics/manual_pioneer.pdf [Link]
18. Dalamagkidis K, Valavanis KP, Piegl AL. Nonlinear model predictive control with neural network optimization for autonomous autorotation of small unmanned helicopters. IEEE Transactions on Control Systems Technology. 2007;19(4):818-831. [Link] [DOI:10.1109/TCST.2010.2054092]
19. Zare-Atabaki A. Model predictive control of mobile robot in presence of obstacles [Dissertation]. Tehran: Iran University of Science and Technology; 2007. [Persian] [Link]

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