Mobile Robot Diagnosis with Bayesian Filters

摘要

In the following paper we consider a problem of fault detection for a mobile robot. Therobot which our work is related to, is based on a new type of the steering principle [1]. The crucialpart of the steering system are axle position sensors. A failure of one of them might result in aninterruption of operation and/or serious damages to hardware and environment elements. To avoidthe risk of such events, a reliable fault detection system has to be implemented. Fault detection isfacilitated by incorporating measurements from various sensors located on board of the robot(incremental encoders, absolute encoders, sonar, cameras, compass, GPS). In our work we considerthe Bayesian approach (Kalman filter [2,3] and particle filters [4,5,6]) to create a diagnostic systemof the robot. Due to the limited resources of the computing unit it is necessary to strongly optimizethe efficiency of applied algorithms. In our work we plan to perform simulations to find the bestsuited algorithm for our vehicle. Accordingly, we build a numerical tool in MATLAB to simulatemobile robot navigation and fault diagnosis tasks. We also present the construction of our robot andexplain how a reliable fault detection system is important for the proper functioning and safety ofthe mobile robot. We introduce the fault detection algorithms which we plan to apply in thedescribed hardware solution. At the end, we summarize our work and provide an outlook on ourfuture research plans