Premium
Using a Self‐Clustering Algorithm and Type‐2 Fuzzy Controller for Multi‐robot Deployment and Navigation in Dynamic Environments
Author(s) -
Jhang JyunYu,
Lee ChinLing,
Lin ChengJian,
Young KuuYoung
Publication year - 2020
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.2283
Subject(s) - software deployment , robot , controller (irrigation) , fuzzy logic , cluster analysis , mobile robot , computer science , process (computing) , mobile robot navigation , real time computing , artificial intelligence , engineering , control theory (sociology) , algorithm , control engineering , robot control , control (management) , agronomy , biology , operating system
This study proposes a novel method for multi‐robot deployment and navigation under dynamic environments. To automatically determine the location deployment of multiple robots, a grid‐based method and self‐clustering algorithm (SCA) were used to simplify the environmental information and automatically deploy robot locations. In the navigation process, a behavior selector automatically turns on towards goal mode or wall‐following mode (WFM) depending on environmental conditions. WFM control adopts an interval type‐2 fuzzy controller (IT2FC). The parameters of the IT2FC are adjusted by using the dynamic group whale optimization algorithm (DGWOA). The proposed DGWOA uses a dynamic group and Lévy flight strategy to overcome the problem of falling into a local minimum solution. Experimental results reveal that the proposed method can successfully complete navigation tasks under dynamic environments.