Decision Tree based diagnosis for hybrid model-based/data-driven fault detection and exclusion of a decentralized multi-vehicle cooperative localization system*

Abstract

Cooperative navigation systems are one of the main topics of interest in multi-robot systems emerging nowadays, where the question of safety remains a very critical one preventing the actual integration of the technology. In this article, a multi-sensor multi-vehicle Cooperative Positioning System (CPS) is presented, with a hybrid fault detection method under a decentralized architecture, and that is tolerant to simultaneous sensor faults. In order to detect and isolate faults, a set of fault sensitive residuals are generated based on the divergence of Jensen Shannon (D JS ) between the probability distributions predicted by the encoder based evolution model and the various observations obtained by sensors. Then, in order to detect a fault, a data-driven approach is applied, where the classification of faults is done by a pre-trained detection decision tree (D-DT) and isolation random forest (I-RF). The testing and evaluation of the approach is done on real data from three Turtlebot3 burger robot equipped with wheel encoders (for prediction), a gyroscope (for the yaw angle) and a Marvelmind (for the absolute position). A ground truth is also recorded using optitrack system.