Adaptive Particle Filter for Unknown Fault Detection of Wheeled Mobile Robots

Fault detection and diagnosis (FDD) is very important for wheeled mobile robots (WMRs). In this paper, an adaptive particle filter is developed to deal with unknown fault detection as well as known fault diagnosis for wheeled mobile robots. Two parameters are extracted from sample-based expression for a posteriori probability density: sum of unnormalized weight of samples, and Kullback-Leiber divergence of proposal distribution and posteriori distribution. Decision rules are derived to determine novel faults based on these parameters. Fault state space is adapted according the number of detecting novel fault. This method preserves the advantages of particle filter and can diagnose known faults as well as detect unknown faults. The method is testified on mobile robot fault diagnosis problem