Journal
ADVANCED ROBOTICS
Volume 25, Issue 16, Pages 2039-2064Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1163/016918611X590238
Keywords
Bayesian network; sensor; fault detection; mobile robot; behavior
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This paper presents FTBN, a new framework that performs learning autonomous mobile robot behavior and fault tolerance simultaneously. For learning behavior in the presence of a robot sensor fault this framework uses a Bayesian network. In the proposed framework, sensor data are used to detect a faulty sensor. Fault isolation is accomplished by changing the Bayesian network structure using interpreted evidence from robot sensors. Experiments including both simulation and a real robot are performed for door-crossing behavior using prior knowledge and sensor data at several maps. This paper explains the learning behavior, optimal tracking, exprimental setup and structure of the proposed framework. The robot uses laser and sonar sensors for door-crossing behavior, such that each sensor can be corrupted during the behavior. Experimental results show FTBN leads to robust behavior in the presence of a sensor fault as well as performing better compared to the conventional Bayesian method. (C) Koninklijke Brill NV, Leiden, 2011
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