Data-driven Anomaly Detection for Quadcopter UAV Indoor Flight Platform

Abstract

Ensuring the safe operation of unmanned aerial vehicles (UAVs) requires timely and accurate detection of anomalies that may indicate system faults or external disturbances. In this study, we propose a data-driven approach for unsupervised anomaly detection in UAVs, leveraging a newly developed multimodal dataset that includes synchronized telemetry, sensor measurements, motion capture data, and pilot inputs. Our method learns representations of normal UAV behavior from healthy flight records and is applied to fault-injection scenarios to identify potential anomalies. Preliminary results on experimental data suggest that the approach can capture subtle deviations from expected behavior across multiple data modalities, including flight dynamics and environmental feedback. This work lays the foundation for data-driven UAV health monitoring through unsupervised learning. It complements our publicly released dataset and analysis tools and aims to facilitate broader research on autonomous anomaly detection, early fault diagnostics, and the development of resilient UAV systems in safety-critical applications.