TY - JOUR
T1 - Navigation Control of Mobile Robots Using an Interval Type-2 Fuzzy Controller Based on Dynamic-group Particle Swarm Optimization
AU - Jhang, Jyun Yu
AU - Lin, Cheng Jian
AU - Lin, Chin Teng
AU - Young, Kuu-Young
PY - 2018/10/1
Y1 - 2018/10/1
N2 - 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.
AB - 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.
KW - Mobile robot
KW - navigation control
KW - particle swarm optimization
KW - type-2 fuzzy neural controller
KW - wall-following control
UR - http://www.scopus.com/inward/record.url?scp=85053468451&partnerID=8YFLogxK
U2 - 10.1007/s12555-017-0156-5
DO - 10.1007/s12555-017-0156-5
M3 - Article
AN - SCOPUS:85053468451
SN - 1598-6446
VL - 16
SP - 2446
EP - 2457
JO - International Journal of Control, Automation and Systems
JF - International Journal of Control, Automation and Systems
IS - 5
ER -