Obstacle Avoidance Control Method of Electric Skid-Steering Chassis Based on Fuzzy Logic Control

doi:10.12133/j.smartag.SA202408003

Abstract

Abstract:

[Objective] Trajectory tracking and obstacle avoidance control are important components of autonomous driving chassis, but most current studies treat these two issues as two independent tasks, which will cause the chassis to stop trajectory tracking when facing an obstacle, and then implement trajectory tracking again after completing obstacle avoidance. If the distance from the reference path after obstacle avoidance is too far, the subsequent tracking performance will be affected. There are also some studies on trajectory tracking and obstacle avoidance at the same time, but these studies are either not smooth enough and prone to chatter, or the control system is too complex. Therefore, a simple algorithm is proposed that can simultaneously implement trajectory tracking and obstacle avoidance control of the chassis in this research. [Methods] First, the kinematic model and kinematic error model of the chassis were designed. Since skid-steering was adopted, the kinematic model of the chassis was simplified to a two-wheel differential rotation robot model when designing the mathematical model. Secondly, the Takagi-Sugeno (T-S) fuzzy controller of the chassis was designed. Since the error model of the chassis was designed in advance, the T-S fuzzy model of the chassis could be designed. Based on the T-S model, a T-S fuzzy controller was designed using the parallel distributed compensation (PDC) algorithm. The linear quadratic regulator (LQR) controller was used as the state feedback controller of each fuzzy subsystem in the T-S fuzzy controller to form a global T-S fuzzy controller, which could realize the trajectory tracking function of the chassis when there were no obstacles. Secondly, the obstacle avoidance controller of the chassis was designed. A new $L Q R o b s$ controller was designed in the global open-loop system to generate the reference trajectory to avoid obstacles. When the system detects an obstacle in the environment, the $L Q R o b s$ controller starts working, and generates a new path by judging the distance between the obstacle and the chassis, so that the chassis could avoid the obstacle. When the chassis bypassed the obstacle, the $L Q R o b s$ controller stopped working. In order to better realize the obstacle avoidance function, a fuzzy controller was designed to adjust the gain matrices Q and R of the $L Q R o b s$ controller in real time. Then, in order to realize trajectory tracking and obstacle avoidance controlled at the same time, a fuzzy fusion controller was designed to combine the two controllers to form the final chassis input, and the Mamdani fuzzy controller was selected to achieve it. Finally, the method was simulated and experimental tested. The simulation test used joint simulation test used MATLAB-Simulink and the experiments based on the self-developed electric multi-functional chassis were conducted. [Results and Discussions] The simulation results showed that when there were no obstacles, the control method could achieve stable trajectory tracking in the reference path composed of straight lines and curves. When there were obstacles, the vehicle could avoid them smoothly and quickly converge to the reference trajectory. When facing obstacles, the designed fuzzy logic $L Q R o b s$ controller could adaptively change the controller gain matrix according to the vehicle's speed and the distance between the current obstacles to achieve rapid convergence. The experimental results showed that when there were no obstacles, the chassis could use the T-S fuzzy controller to achieve stable tracking of the reference trajectory, and the average errors in the lateral and longitudinal directions of the entire tracking process were 0.041 and 0.052 m, respectively. When facing obstacles, the T-S fuzzy controller and the $L Q R o b s$ controller realized the obstacle avoidance and tracking control of the chassis through joint control. The fuzzy controller was used to adjust the gain matrix of the $L Q R o b s$ controller in real time, and the tracking error was reduced by 33.9% compared with the controller with a fixed gain matrix. [Conclusions] The control system can simultaneously realize the trajectory tracking and obstacle avoidance control of the chassis, can quickly converge the tracking error to zero, and achieve smooth obstacle avoidance control. Although the control method proposed is simple and efficient, and the tracking and obstacle avoidance effects are significantly improved, the control method can only handle static obstacles on the reference path at present, and subsequent research will focus on dynamic obstacles.

Key words: skid steering, trajectory tracking, obstacle avoidance control, LQR, fuzzy logic control

CLC Number:

TP272

LI Lei, SHE Xiaoming, TANG Xinglong, ZHANG Tao, DONG Jiwei, GU Yuchuan, ZHOU Xiaohui, FENG Wei, YANG Qinghui. Obstacle Avoidance Control Method of Electric Skid-Steering Chassis Based on Fuzzy Logic Control[J]. Smart Agriculture, 2026, 8(1): 213-225.

Figures/Tables 27

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Fig. 2

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Fig. 4

Table 1

Parameters setting of gain matrix of the L Q R o b s obstacle avoidance

名称	类别	基本论域	量化等级
$D o b$	输入	［0 m， 0.5 m］	｛0 m， 0.25 m， 0.5 m｝ = ｛PS， PM， PB｝
$v T$	输入	［0 m/s， 1.4 m/s］	｛0 m/s， 0.4 m/s， 1.4 m/s｝ = ｛PS， PM， PB｝
$q 1$	输出	［0， 3］	｛0， 0.4， 1， 3｝ = ｛ZE， PS， PM， PB｝
$q 2$	输出	［0， 3］	｛0， 0.4， 1， 3｝ = ｛ZE， PS， PM， PB｝
$q 3$	输出	［0， 3］	｛0， 0.5， 0.8， 3｝ = ｛ZE， PS， PM， PB｝
$r 1$	输出	［0， 0.014］	｛0， 0.004， 0.006， 0.014｝ = ｛ZE， PS， PM， PB｝
$r 2$	输出	［0， 0.014］	｛0， 0.004， 0.006， 0.014｝ = ｛ZE， PS， PM， PB｝

Table 1

Fig. 5

Table 2

Fuzzy rule of gain matrix of the L Q R o b s obstacle avoidance

规则数	输入		输出
规则数	$D o b$	$v T$	$q 1$	$q 2$	$q 3$	$r 1$	$r 2$
1	PS	PS	PB	PB	PB	ZE	ZE
2	PS	PS	PB	PB	PB	PS	PS
3	PS	PS	PB	PB	PB	PB	PB
4	PM	PM	PS	PS	PS	PM	PM
5	PM	PM	PM	PM	PM	PM	PM
6	PM	PM	PB	PB	PB	PB	PB
7	PB	PB	ZE	ZE	ZE	PB	PB
8	PB	PB	ZE	ZE	ZE	PB	PB
9	PB	PB	ZE	ZE	ZE	PB	PB

Table 2

Fig. 6

Table 3

The fuzzy rules of the fuzzy fusion control

$θ o b s$	$D o b s$
$θ o b s$	PS	PB
NB	PB	PS
PS	PM	PS
PB	PB	PS

Table 3

Table 4

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Table 5

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参数名称	单位	数值
整机形式	/	轮式
外形尺寸	mm	1 500×1 200×800
装备质量	kg	200
载重	kg	100
平均最高车速	km/h	5
轴距	mm	800
轮距	mm	1 100
虚拟轴距	mm	1 250
LQR增益矩阵 Q	/	diag（1，1，1）
LQR增益矩阵 R	/	diag（0.005，0.005）

工况	横向平均跟踪误差/m	纵向平均跟踪误差/m	直线平均横向跟踪误差/m	直线平均纵向跟踪误差/m	曲线平均横向跟踪误差/m	曲线平均纵向跟踪误差/m
无障碍物	0.036 5	0.048 2	0.021	0.030	0.065	0.078
有障碍物	0.189	0.223	0.023	0.028	0.532	0.489