NDLI: Unknown Fault Detection for Mobile Robots Based on Particle Filters

Content Provider	IEEE Xplore Digital Library
Author	Zhuohua Duan Zixing Cai Jinxia Yu
Copyright Year	2006
Description	Author affiliation: Sch. of Inf. Eng., Shaoguan Univ., Guangdong (Zhuohua Duan)
Abstract	An improved particle filter was presented to simultaneously detect unknown faults and diagnose known faults for mobile robots. Firstly, the kinematics and fault models of the monitored mobile robot were given. Secondly, two parameters were extracted from sample-based expression for a posteriori probability density: sum of sample weights, and reliability of a posteriori belief state. These features were used to detect whether the estimation given by particle filter was believable or not. Unbelievable estimation indicates that the true state was not in the current state space, i.e. it is a novel state (or a unknown fault). This method preserves the advantages of particle filters and can diagnose known faults as well as detect unknown fault. The method is testified on a real mobile robot
Starting Page	5452
Ending Page	5456
File Size	351465
Page Count	5
File Format	PDF
ISBN	1424403324
DOI	10.1109/WCICA.2006.1714114
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault detection Mobile robots Particle filters Fault diagnosis Monitoring State-space methods State estimation Testing Humans Gaussian noise Unknown fault Mobile robot Fault detection and diagnosis Particle filter
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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