Anomaly Management in Unmanned Aerial Vehicles: A Systematic Literature Review

Abstract

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in safety-critical applications such as logistics, surveillance, disaster response, and urban air mobility. While their autonomy enables powerful capabilities, it also introduces vulnerabilities due to hardware faults, software defects, communication failures, and adversarial interference. This survey presents a comprehensive review of research studies closely related to UAV anomalies published between 2015 and 2025, covering 111 papers from academic and industrial sources. We introduce a unified five-pillar taxonomy—anomaly generation, prevention, detection, recovery, and analysis—that organizes existing work across the full anomaly management lifecycle. In contrast to prior surveys that focus primarily on detection algorithms, our review integrates operational and regulatory perspectives, explicitly linking technical anomalies to policy enforcement and compliance requirements. We systematically compare detection techniques, datasets, simulators, and evaluation practices, revealing significant fragmentation in datasets, limited real-world validation, and a lack of standardized real-time benchmarks. Our synthesis highlights key research challenges, including the sim-to-real gap, limited interpretability of learning-based detectors, and the scarcity of policy-aware anomaly management frameworks. Based on these findings, we outline emerging research opportunities for adaptive, explainable, and benchmark-driven anomaly management systems that support safe, transparent, and reliable UAV operations. We release our metadata for all the papers reviewed, as well as filters for easy sorting.