DocumentCode :
1126545
Title :
A fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance
Author :
Ye, Cang ; Yung, Nelson H C ; Wang, Danwei
Author_Institution :
Adv. Technol. Lab., Univ. of Michigan, Ann Arbor, MI, USA
Volume :
33
Issue :
1
fYear :
2003
fDate :
2/1/2003 12:00:00 AM
Firstpage :
17
Lastpage :
27
Abstract :
Fuzzy logic systems are promising for efficient obstacle avoidance. However, it is difficult to maintain the correctness, consistency, and completeness of a fuzzy rule base constructed and tuned by a human expert. A reinforcement learning method is capable of learning the fuzzy rules automatically. However, it incurs a heavy learning phase and may result in an insufficiently learned rule base due to the curse of dimensionality. In this paper, we propose a neural fuzzy system with mixed coarse learning and fine learning phases. In the first phase, a supervised learning method is used to determine the membership functions for input and output variables simultaneously. After sufficient training, fine learning is applied which employs reinforcement learning algorithm to fine-tune the membership functions for output variables. For sufficient learning, a new learning method using a modification of Sutton and Barto´s model is proposed to strengthen the exploration. Through this two-step tuning approach, the mobile robot is able to perform collision-free navigation. To deal with the difficulty of acquiring a large amount of training data with high consistency for supervised learning, we develop a virtual environment (VE) simulator, which is able to provide desktop virtual environment (DVE) and immersive virtual environment (IVE) visualization. Through operating a mobile robot in the virtual environment (DVE/IVE) by a skilled human operator, training data are readily obtained and used to train the neural fuzzy system.
Keywords :
collision avoidance; fuzzy control; fuzzy set theory; intelligent control; learning (artificial intelligence); mobile robots; neurocontrollers; virtual reality; desktop virtual environment visualization; fuzzy controller; fuzzy logic system; fuzzy rule base; immersive virtual environment visualization; input variables; membership functions; mixed coarse/fine learning phases; mobile robot; neural fuzzy system; obstacle avoidance; output variables; skilled human operator; supervised learning assisted reinforcement learning algorithm; tuning; Fuzzy control; Fuzzy logic; Fuzzy systems; Humans; Learning systems; Mobile robots; Navigation; Supervised learning; Training data; Virtual environment;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publisher :
ieee
ISSN :
1083-4419
Type :
jour
DOI :
10.1109/TSMCB.2003.808179
Filename :
1167350
Link To Document :
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