Real Time Path Predicting for Autonomous Underwater Vehicle Using Support Vector Regression Machines

Autonomous underwater vehicles (AUV) are unmanned underwater vessels to investigate sea environments, oceanography and deep-sea resources autonomously. Navigation of underwater vehicles is a very demanding task, especially in confined environment. In order to avoid different risk, the path informati...

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Bibliographic Details
Published in2008 Fourth International Conference on Natural Computation Vol. 4; pp. 414 - 416
Main Authors Rubo Zhang, Guanqun Liu, Xucyao Li
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2008
Subjects
Acoustic sensors
Automotive engineering
Autonomous underwater vehicle
Computer science
Educational institutions
Marine technology
Path prediction
Predictive models
Support vector machines
Support vector regression machines
Time series prediction
Training data
Underwater vehicles
Yttrium
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Summary:Autonomous underwater vehicles (AUV) are unmanned underwater vessels to investigate sea environments, oceanography and deep-sea resources autonomously. Navigation of underwater vehicles is a very demanding task, especially in confined environment. In order to avoid different risk, the path information must be communicated between sensors and monitor immediately, but the data transfer rate are very low in the sub-sea acoustic communication channel, it is impossible to receive these information immediately, so a model based on support vector regression machines for real time path predicting is proposed in this paper. This model can update according the new data form the sensors. Experiments show this method can have a good performance to predict the path of AUV.
ISBN:9780769533049
0769533043
ISSN:2157-9555
DOI:10.1109/ICNC.2008.380