Fault-tolerant multi-robot localization: diagnostic decision-making with information theory and learning models

Abstract

In the domain of multi-robot systems, cooperative systems that are highly attuned and connected to their surroundings are becoming increasingly significant. This surge in interest highlights various challenges, especially regarding system integration and safety constraints. Our research contributes to the assurance of fault tolerance to avert abnormal behaviors and sustain reliable robot localization. In this paper, a mixed approach between data-driven and model-based for fault detection is introduced, within a decentralized architecture, thereby strengthening the system’s capacity to handle simultaneous sensor faults. Information theory-based fault indicators are developed by computing the Jensen-Shannon divergence (\(D_{JS}\)) between state predictions and sensor-obtained corrections. This initiates a two-tiered data-driven mechanism: one layer employing Machine Learning for fault detection, and another distinct layer for fault isolation. The methodology’s efficacy is assessed using real data from the Turtlebot3 platform.