Navigation Control of Mobile Robots Using an Interval Type-2 Fuzzy Controller Based on Dynamic-group Particle Swarm Optimization

被引：0

作者：

Jyun-Yu Jhang

Cheng-Jian Lin

Chin-Teng Lin

Kuu-Young Young

机构：

[1] National Chiao Tung University,Institute of Electrical and Control Engineering

[2] National Chin-Yi University of Technology,Department of Computer Science and Information Engineering

[3] National Chiao Tung University,Department of Electrical and Computer Engineering

[4] University of Technology Sydney,Centre for Artificial Intelligence, FEIT

来源：

International Journal of Control, Automation and Systems | 2018年 / 16卷

关键词：

Mobile robot; navigation control; particle swarm optimization; type-2 fuzzy neural controller; wall-following control;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

This paper presents an effective navigation control method for mobile robots in an unknown environment. The proposed behavior manager (BM) switches between two behavioral control patterns, wall-following behavior (WFB) and toward-goal behavior (TGB), based on the relationship between the mobile robot and the unknown environment. An interval type-2 fuzzy neural controller with a dynamic-group particle swarm optimization (DGPSO) algorithm is proposed to provide WFB control and obstacle avoidance for mobile robots. In the WFB learning process, the input signal of a controller is the distance between the wall and the sonar sensors, and its output signal is the speed of two wheels of a mobile robot. A fitness function, which operates on the total distance traveled by the mobile robot, distance from the side wall, angle to the side wall, and moving speed, evaluates the WFB performance of the mobile robot. In addition, an escape mechanism is proposed to avoid a dead cycle. Experimental results reveal that the proposed DGPSO is superior to other methods in WFB and navigation control.

引用

页码：2446 / 2457

页数：11

共 49 条

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