Motion control of differential robot based on speed adjusting and path tracking

被引:0
|
作者
Zhang J. [1 ]
Bai G. [2 ]
机构
[1] Shanxi Institute of Mechanical and Electrical Engineering, Changzhi
[2] School of Mechanical Engineering, University of Science and Technology Beijing, Beijing
关键词
differential robot; motion control; path tracking; speed adjusting; unmanned driving;
D O I
10.13374/j.issn2095-9389.2022.08.14.003
中图分类号
学科分类号
摘要
A differential robot is a typical mobile robot widely used in storage, agriculture, and other industries. The motion control of differential robots, including longitudinal and lateral control, is a current research hotspot. To date, researchers have not paid much attention to the interaction between longitudinal and lateral control of differential robots. However, the conflict between the ability to track the reference path and maintain the longitudinal speed at its maximum value is a critical issue that limits the operational efficiency of the differential robot. To solve this problem, a mapping relationship between the longitudinal speed and the turning curvature is analyzed. The mapping relationship is established when the maximum value of the longitudinal speed is known, i.e., the feasible upper limit of the longitudinal speed that can guarantee the steering ability of the differential robot is inversely proportional to the curvature of the trajectory. From this mapping relationship, a speed-adjusting method is proposed based on the preview information. This speed-adjusting method consists of two steps. First, the smaller value between the upper limit of the feasible longitudinal speed in a certain preview distance and the set value of the longitudinal speed are taken as the desired longitudinal speed. Second, a control law is established based on the deviation between this desired and current longitudinal speed. Additionally, a path-tracking method that cooperates with the above-mentioned speed-adjusting method is proposed. The theoretical basis of this path-tracking method is a nonlinear model predictive control. The prediction model used in this control method is derived from a kinematic model with longitudinal speed as a time-dependent parameter. Finally, a differential robot motion control system is formed based on speed adjusting and path tracking. The simulation and experimental results show that the proposed motion control system can actively adjust the longitudinal speed when the set value of the longitudinal speed of the differential robot is high and ensure high accuracy of path tracking control. Furthermore, the absolute value of the displacement and heading errors does not exceed 0.0499 m and 0.0726 rad, which are reduced by 97.57% and 45.04% compared with the motion control system without speed adjusting, respectively. © 2023 Science Press. All rights reserved.
引用
收藏
页码:1550 / 1558
页数:8
相关论文
共 30 条
  • [11] Orita Y, Fukao T., Robust human tracking of a crawler robot, J Robot Mechatron, 31, 2, (2019)
  • [12] Li Y C, Qiao Y., Design and simulation of path tracking controller for four-wheel robot, Electron Meas Technol, 42, 13, (2019)
  • [13] Zhao Z Y, Liu H O, Chen H Y, Et al., Kinematics-aware model predictive control for autonomous high-speed tracked vehicles under the off-road conditions, Mech Syst Signal Process, 123, (2019)
  • [14] Yang H J, Wang S Z, Zuo Z Q, Et al., Trajectory tracking for a wheeled mobile robot with an omnidirectional wheel on uneven ground, IET Control Theory Appl, 14, 7, (2020)
  • [15] Bai G X, Liu L, Meng Y, Et al., Real-time path tracking of mobile robot based on nonlinear model predictive control, Trans Chin Soc Agric Mach, 51, 9, (2020)
  • [16] Ibraheem G A R, Azar A T, Ibraheem I K, Et al., A novel design of a neural network-based fractional PID controller for mobile robots using hybridized fruit fly and particle swarm optimization, Complexity, 2020, (2020)
  • [17] Khalaji A K, Jalalnezhad M., Robust forwardbackward control of wheeled mobile robots, ISA Trans, 115, (2021)
  • [18] Zhao L, Jin J, Gong J Q., Robust zeroing neural network for fixed-time kinematic control of wheeled mobile robot in noise-polluted environment, Math Comput Simul, 185, (2021)
  • [19] Gao X S, Gao R, Liang P, Et al., A hybrid tracking control strategy for nonholonomic wheeled mobile robot incorporating deep reinforcement learning approach, IEEE Access, 9, (2021)
  • [20] Liu Z J, Wang X L, Ren Z G, Et al., Crawler tractor navigation path tracking control algorithm based on virtual radar model, Trans Chin Soc Agric Mach, 52, 6, (2021)