DocumentCode
411595
Title
A reinforcement-learning approach to robot navigation
Author
Su, Mu-Chun ; Huang, De-Yuan ; Chou, Chien-Hsing ; Hsieh, Chen-Chiung
Author_Institution
Dept. of Comput. Sci & Inf. Eng., Nat. Central Univ., Chung-li, Taiwan
Volume
1
fYear
2004
fDate
21-23 March 2004
Firstpage
665
Abstract
This paper presents a reinforcement-learning approach to a navigation system which allows a goal-directed mobile robot to incrementally adapt to an unknown environment. Fuzzy rules which map current sensory inputs to appropriate actions are built through the reinforcement learning. Simulation results illustrate the performance of the proposed navigation system. In this paper, ACSNFIS is used as the main network architecture to implement the reinforcement-learning based navigation system.
Keywords
fuzzy neural nets; fuzzy set theory; learning (artificial intelligence); mobile robots; navigation; classifier system based neurofuzzy inference system; fuzzy rules; goal directed mobile robot; navigation system; reinforcement learning; robot navigation; Computer architecture; Computer science; Fuzzy neural networks; Fuzzy systems; Inference algorithms; Machine learning algorithms; Mobile robots; Navigation; Path planning; Service robots;
fLanguage
English
Publisher
ieee
Conference_Titel
Networking, Sensing and Control, 2004 IEEE International Conference on
ISSN
1810-7869
Print_ISBN
0-7803-8193-9
Type
conf
DOI
10.1109/ICNSC.2004.1297519
Filename
1297519
Link To Document