Machine Learning-Based Predictive Maintenance for Ground Robotics

Abstract

Predictive maintenance (PdM) is increasingly essential for the reliable operation of ground robotic systems, which often operate in complex and challenging environments. This paper presents a machine learning-based framework for predictive maintenance in ground robotics, enabling early detection of potential failures and the optimization of maintenance schedules. By analyzing real-time sensor data and operational patterns, the proposed system uses machine learning algorithms to predict when critical components are likely to fail, allowing for preemptive interventions that reduce downtime and repair costs. The framework integrates sensor fusion, anomaly detection, and prognostics to provide a comprehensive maintenance strategy. We conducted extensive testing on various ground robotic platforms to validate the accuracy and reliability of the model. The results demonstrate improved system longevity, increased operational efficiency, and reduced unplanned failures. This machine learning approach offers a transformative step towards autonomous, data-driven maintenance practices in robotics.