Fault detection for service mobile robots using model-based method

Detection of faults is a topic of high importance because it increases robot dependability, a requirement for the wide acceptance of service robots in domestic environments. This work takes a model-based approach for detecting and identifying actuator faults on differential-drive mobile robots in an indoor environment. An error-bound is calculated between the estimated and measured robot states which is constantly adapted based on the current state and input signals. A fault is detected when the estimation error is outside this bound. The model parameters are learned by the robot using an adaptive law, after the robot deployment in the target environment. Model uncertainties have an important impact on the fault detection performance, and are dealt with by considering the uncertainty bounds in the bound calculations. This ensures no false alarms occur when the uncertainty remains bounded during normal operation. Furthermore an extension to the method is proposed that addresses the problem of detecting small faults. The method is experimentally validated on a iRobot Roomba autonomous robot.