Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/43905
標題: Evolutionary-Group-Based Particle-Swarm-Optimized Fuzzy Controller With Application to Mobile-Robot Navigation in Unknown Environments
作者: Juang, Chia-Feng
Chang, Yu-Cheng
關鍵字: Dead-cycle problem
fuzzy-system optimization
genetic algorithms (GAs)
particle-swarm optimization (PSO)
robot navigation
出版社: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC.
摘要: This paper proposes an evolutionary-group-based particle-swarm-optimization (EGPSO) algorithm for fuzzy-controller (FC) design. The EGPSO uses a group-based framework to incorporate crossover and mutation operations into particle-swarm optimization. The EGPSO dynamically forms different groups to select parents in crossover operations, particle updates, and replacements. An adaptive velocity-mutated operation (AVMO) is incorporated to improve search ability. The EGPSO is applied to design all of the free parameters in a zero-order Takagi-Sugeno-Kang (TSK)-type FC. The objective of EGPSO is to improve fuzzy-control accuracy and design efficiency. Comparisons with different population-based optimizations of fuzzy-control problems demonstrate the superiority of EGPSO performance. In particular, the EGPSO-designed FC is applied to mobile-robot navigation in unknown environments. In this application, the robot learns to follow object boundaries through an EGPSO-designed FC. A simple learning environment is created to build this behavior without an exhaustive collection of input-output training pairs in advance. A behavior supervisor is proposed to combine the boundary-following behavior and the target-seeking behavior for navigation, and the problem of dead cycles is considered. Successful mobile-robot navigation in simulation and real environments verifies the EGPSO-designed FC-navigation approach.
URI: http://hdl.handle.net/11455/43905
ISSN: 1063-6706
Appears in Collections:電機工程學系所

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