Simulation-Generated Data for Fault Detection and Diagnosis in Mobile Robots

Abstract

Fault detection and diagnosis is a crucial task for modern mobile robots, as it permits their correct functioning with a positive impact on availability, autonomy and safety. Despite several approaches of fault detection and diagnosis of sensor faults in mobile robots, there exists a lack of scientific studies addressing the low-cost LiDAR faults which are commonly adopted in indoor robots. This paper is a preliminary attempt to fill this research gap by proposing a data-driven approach for developing a diagnostic module for mobile robots, focusing on the specific case study of the Stretch robot.Several LiDAR faults are first modeled, then simulated in ROS and Gazebo environments to generate a high-quality dataset. The dataset is used for training a machine learning model, which can diagnose such faults as well as distinguish them from other typical faults, such as IMU pose drift.Our proposed model shows high detection and isolation accuracies across the injected fault scenarios, thus paving the way for the deployment on a real robotic system for further evaluation and analysis.