Online Fault Detection for Four Wheeled Skid Steered UGV Using Neural Network

This paper proposes a neural network-based actuator fault detection scheme for four-wheeled skid-steered unmanned ground vehicles (UGV). The neural network approach is first validated on vehicle dynamics simulations. Then, it is tailored for the experimental setup. Experiments involve a motion tracking system, Husarion Rosbot 2.0 UGV with associated network control systems. For experimental work, the disturbance is intentionally induced by augmenting wheels with a bump. Network size optimization is also carried out so that computing resource is saved without degrading detecting accuracy too much. The resulting network exhibit fault detection and isolation accuracy over 97% of the test data. A scenario is experimentally illustrated where a fault occurs, is detected, and tracking control is modified to continue operation in the presence of an actuator fault.