Unmonitored Fault Detection in an AUV Using Recurrent Neural Networks
Faults are an inevitability in any robotic system but are of a particularly devastating nature in underwater robotics. If unrealized, a minor fault in an autonomous underwater vehicle (AUV) could easily lead to the AUV unintentionally damaging itself, an accompanying diver, or the marine environment in which it is operating. While some types of faults can easily be detected with sensors on the AUV, it is costly or impractical to have sensors installed to monitor every potential point of failure on an AUV. In this paper, we present a software system that enables an AUV to detect unmonitored faults via the fault's indirect effects on the AUV's normal motion. This system is composed of two primary components: a recurrent neural network(RNN) based model that predicts the future motion of an AUV and an anomaly detection algorithm that is able to detect anomalies in the motion prediction error and decide whether the anomalies indicate a fault occurred. To evaluate the performance of this system, we demonstrate its effectiveness on data collected on an AUV in both pool and field trials.