Particle filter-based fault diagnosis for inertial navigation system of mobile robot

Abstract

A particle filter-based approach for fault diagnosis of inertial sensor of wheeled mobile robots was proposed, in which the rule-based inference and multiple particle filters were integrated. The rule-based inference method was employed to determine the movement states of the robot. Each movement state was monitored by a particle filter. This approach overcomes some shortcomings of particle filter such as weak logic inference capability, decreases particle number, increases efficiency and accuracy of each particle filter. The results of monitoring 8 kinds of operation mode of inertial navigation system (INS) of 5 kinds of movement states of mobile robot in a plane show that the approach presented can diagnose one or more hard faults of intertial navigation system sensors of mobile robots.