Unsupervised fault detection and recovery for intelligent robotic rollators

Abstract

Fault diagnosis is a key safety component in robotic assistive technologies. Although conventional model-based methods for sensor fault diagnosis in mobile robots have been well established, they face challenges due to model parameter changes and uncertainties. On the other hand, data-driven approaches becomes more appealing in order to take advantage from available historical data in the era of Big Data. To provide a new generic unsupervised solution to the fault detection and recovery, we explicitly include kinematic relations and temporal finite differences from measured sensor signals into training a multi-task deep neural network. To evaluate the proposed fault diagnosis and recovery framework, experiments have been conducted on a robotic rollator platform. Experiments under several conditions confirm that the proposed approach, which leverages machine learning-enhanced algorithms, exhibits reliable performance. Outperforming other baselines and state-of-the-art diagnosis algorithms, the framework presents a promising solution to sensor fault recovery challenges in assistive devices.