Fault Diagnosis for Mobile Robots with Imperfect Models Based on Particle Filter and Neural Network
Fault detection and diagnosis (FDD) are increasingly important for wheeled mobile robots (WMRs), especially those in unknown environments such as planetary exploration. There are many kinds of fault diagnosis methods available for mobile robots, including multiple model-based approaches, particle filter based approaches, sensor fusion based approaches. Currently, all of these methods are designed for complete models. However, completely modeling a system is difficult, even impossible. In this paper, particle filter and neural network are integrated to diagnose complex systems with imperfect models. Two features are extracted from particles: the sum of sample weights, and the maximal a posteriori probability. These features are further feed to a neural network to decide whether the estimation given by the particle filter is credible or not. An incredible estimation indicates that the true state isn’t included in the state space, i.e. it is a novel state (or an unknown fault). This method preserves the merits of particle filter and can diagnose known faults as well as detect unknown faults. It is testified on a real mobile robot.
KeywordsMobile Robot Fault Diagnosis Particle Filter Belief State Multi Layer Perceptron
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