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Abstract

In order to improve the trajectory tracking control effect of mobile robot, a mobile robot trajectory tracking control (TRC) method based on AVRX operating system is proposed. The principle of AVRX operating system is analyzed and the hardware structure of mobile robot is designed. The input-output relationship of mobile robot trajectory is determined according to multivariable fuzzy controller and the wrong data of mobile robot trajectory tracking information is eliminated by radar point cloud measurement. The TRC model of mobile robot is designed through Euler Lagrange equation to realize the TRC of mobile robot. The experimental results show that the trajectory tracking accuracy of mobile robot is as high as 97.9%, the trajectory tracking time is 16.2 s, and the trajectory tracking error of mobile robot is only 0.04. It shows that the TRC effect of mobile robot is improved, which provides a reference for the trajectory tracking control of mobile robots.

Keywords

Euler lagrange equation point cloud measurement multivariable fuzzy controller AVRX operating system mobile robot trajectory tracking control

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References

Mao

Zhang

Qiu

. Robot TRC based on multi-sensor fusion. Control Eng. 2020; 27(7): 1125-1130.

Singhirunnusorn

Fahimi

Aygun

. A single camera 360-degree real time vision-based localization method with application to mobile robot trajectory tracking. IET Cyber-Syst Rob. 2021; 18(5): 87-99.

Zhang

Liu

Zhang

. Design, implementation, and validation of robust fractional-order PD controller for wheeled mobile robot trajectory tracking. Complexity. 2020; 20(28): 56-68.

Wang

Duan

. Learning-based kinematic control using position and velocity errors for robot trajectory tracking. IEEE Trans Syst, Man, Cybern: Syst. 2022; 52(2): 1100-1110.

Majd

Jahromi

Homaifar

. A stable analytical solution method for car-like robot trajectory tracking and optimization. IEEE/CAA J Auto Sin. 2020; 7(1): 42-50.

Morlock

Meyer

Pick

Seifried

. Real-time TRC of a parallel robot with flexible links. Mech Mach Theory. 2021; 158(8): 208-220.

Han

Wang

Dong

. TRC of cooperative robot based on Udwadia kalaba theory. Modular Mach Tools Auto Mach Technol. 2021; 18(1): 78-83.

Han

Liu

. Research on trajectory error optimization of industrial robot based on B-spline curve. Chin J Constr Mach. 2020; 18(3): 199-204.

Xiao

Han

Zhao

. Dual loop TRC of wheeled robot based on sliding mode variable structure. Mech Des Manuf. 2020; 16(4): 244-247.

10.

Reynoso

FDP

Suarez

PAN

Sanchez

OFA

Yañez

MBC

Alvarado

Flores

EAP

. A custom EOG-based HMI using neural network modeling to real-time for the trajectory tracking of a manipulator robot. Front Neurorobot. 2020; 14(28): 818-834.

11.

Xiao

Shen

. Modified linear active disturbance rejection control for uncertain robot manipulator trajectory tracking. Math Probl Eng. 2021; 20(2): 1-13.

12.

Ding

Wang

Chen

. An improved robot trajectory planning method considering torque saturation and modeling errors. Trans-Can Soc Mech Eng. 2020; 44(2): 1-15.

13.

Uddin

. A two-wheeled robot TRC system design based on poles domination approach. IAENG Int J Comput Sci. 2020; 47(2): 87-96.

14.

Cao

Pan

Zhang

. Trajectory tracking of wall climbing robot based on improved nonlinear disturbance observer. Comput Syst Appl. 2021; 30(2): 28-34.

15.

Teng

Zhuang

Deng

. Double loop TRC of wall climbing robot based on global stability. Comput Eng Des. 2020; 41(9): 2636-2642.

16.

Huang

. Research on robot terminal TRC based on visual feedback. Instrum Technol Sens. 2021; 18(8): 95-98.

17.

Wang

Song

. Time optimal trajectory planning of industrial robot based on IAGA. Comput Simul. 2021; 38(8): 368-371.

Trajectory tracking control method of mobile robot based on AVRX operating system

Abstract

Keywords

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References