Particle Filters Based Fault Diagnosis for Internal Sensors of Mobile Robots

Fault diagnosis is a challengeable problem for wheeled mobile robots (WMRs). In this paper, domain constrains and particle filters are integrated to diagnose faults of internal sensors of WMRpsilas. The domain constrains are used employed to determine the states of the movement of a wheel mobile robot, MORCS-1, and every movement state is monitored with an adaptive particle filter, which adjust the particle numbers according to the size of state space. The paper presents a general framework to combine domain knowledge with particle filters. The key advantage of the proposed method is that it decreases the size of the state space for each particle filter. As a result, it decreases particle number and increases efficiency and accuracy for each particle filter. Experiment performed on a mobile robot shows the improvement in accuracy and efficiency.