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Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Related citation:YangGe,ZHANG Ru-bo,XuDong.Implementation of AUV local planning in strong sea flow field based on Q-learning[J].Journal of Harbin Institute Of Technology(New Series),2011,18(1):23-28.DOI:10.11916/j.issn.1005-9113.2011.01.005.
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Implementation of AUV local planning in strong sea flow field based on Q-learning
Author NameAffiliation
YangGe School of Computer Science and Technology,Harbin Engineering University,Harbin 150001,China 
ZHANG Ru-bo School of Computer Science and Technology,Harbin Engineering University,Harbin 150001,China 
XuDong School of Computer Science and Technology,Harbin Engineering University,Harbin 150001,China 
Abstract:
This article investigated the implementation of AUV local planning under the strong sea flow field by integrating Q learning with fuzzy logic method.The dynamics of AUV under the sea flow was analyzed in detail,and thus fuzzy logic behaviors were defined,including a fuzzy behavior which was defined to resist the sea flow by giving an extra angle towards sea flow.This behavior was complemented by two other behaviors,the moving-to-goal behavior and collision avoiding behavior.The recommendations of these three behaviors were integrated through adjustable weighting factors to generate the final motion command for the AUV.And Q-learning was used to adjust the peak point of fuzzy membership function to increase adaptability.Simulation results showed that it improves the adaptability of AUV under different sea flow greatly.
Key words:  Q-leaning  fuzzy logic  AUV  navigation  ocean current
DOI：10.11916/j.issn.1005-9113.2011.01.005
Clc Number:P715.5
Fund:

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