Reliable navigation is a very important part of an autonomous mobile robot system. This means for instance that the robot should not lose track of its position, even if unexpected events like wheel slip and collisions occur. The standard approach to this problem is to construct a navigation system that is robust in itself. This paper proposes that detecting faults can also be made outside the normal navigation system, as an additional fault detector. Besides increasing the robustness, a means for detecting deviations is obtained, which can be important for the rest of the robot system, for instance the top level planner. The method uses two or more sources of robot position estimates, and compares them to detect unexpected deviation without getting deceived by drift or different characteristics in the position systems it gets information from. Both relative and absolute position sources can be used, meaning that existing positioning systems already implemented can be used in the detector. For detection purposes, an extended Kalman filter is used in conjunction with a CUSUM test. The detector is able to not only detect faults, but also give an estimate of when the fault occurred, which is useful for doing fault recovery. The detector is easy to implement, as it requires no modification of existing systems. Also the computational demands are very low. The approach is implemented and demonstrated on a mobile robot, using odometry and a scan matcher as sources of position information. It is shown that the system is able to detect wheel slip in real-time
['Paul Sundvall', 'Patric Jensfelt']
Fault detection for mobile robots using redundant positioning systems