Advances in UAV Avionics Systems Architecture, Classification and Integration: A Comprehensive Review and Future Perspectives
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Abstract (arXiv)
Avionics systems of an Unmanned Aerial Vehicle (UAV) or drone are the critical electronic components found onboard that regulate, navigate, and control UAV travel while ensuring public safety. Contemporary UAV avionics work together to facilitate success of UAV missions by enabling stable communication, secure identification protocols, novel energy solutions, multi-sensor accurate perception and autonomous navigation, precise path planning, that guarantees collision avoidance, reliable trajectory control, and efficient data transfer within the UAV system. Moreover, special consideration must be given to electronic warfare threats prevention, detection, and mitigation, and the regulatory framework associated with UAV operations. This review presents the role and taxonomy of each UAV avionics system while covering shortcomings and benefits of available alternatives within each system. UAV communication systems, antennas, and location communication tracking are surveyed. Identification systems that respond to air-to-air or air-to-ground interrogating signals are presented. UAV classical and more innovative power sources are discussed. The rapid development of perception systems improves UAV autonomous navigation and control capabilities. The paper reviews common perception systems, navigation techniques, path planning approaches, obstacle avoidance methods, and tracking control. Modern electronic warfare uses advanced techniques and has to be counteracted by equally advanced methods to keep the public safe. Consequently, this work presents a detailed overview of common electronic warfare threats and state-of-the-art countermeasures and defensive aids. UAV safety occurrences are analyzed in the context of national regulatory framework and the certification process. Databus communication and standards for UAVs are reviewed as they enable efficient and fast real-time data transfer.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jan 2025.
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Citing papers per year: 2025: 5; 2026: 2.
7 papers.
- 2025SkyVLN: Vision-and-Language Navigation and NMPC Control for UAVs in Urban Environmentscited by 14
- 2025MoCom: Motion-based Inter-MAV Visual Communication Using Event Vision and Spiking Neural Networkscited by 3
- 2025A Comparative Analysis of Ensemble-Based Machine Learning Approaches with Explainable AI for Multi-Class Intrusion Detection in Drone Networkscited by 1
- 2025MAVR-Net: Robust Multi-View Learning for MAV Action Recognition with Cross-View Attentioncited by 1
- 2025A Physics-Informed Neural Network Approach for UAV Path Planning in Dynamic Environmentscited by 0
- 2026Learned Memory Attenuation in Sage-Husa Kalman Filters for Robust UAV State Estimationcited by 0
- 2026TileNet: Tile-Based CNN-SVM Architecture for Autonomous Unmanned Aerial Systems Inspection of Flat Roofscited by 0
- 2026TileNet: Tile-Based CNN-SVM Architecture for Autonomous Unmanned Aerial Systems Inspection of Flat Roofs11 Sep 2026
- 2026Learned Memory Attenuation in Sage-Husa Kalman Filters for Robust UAV State Estimation18 May 2026
- 2025A Physics-Informed Neural Network Approach for UAV Path Planning in Dynamic Environments23 Oct 2025
- 2025MAVR-Net: Robust Multi-View Learning for MAV Action Recognition with Cross-View Attention17 Oct 2025
- 2025MoCom: Motion-based Inter-MAV Visual Communication Using Event Vision and Spiking Neural Networks16 Oct 2025
- 2025A Comparative Analysis of Ensemble-Based Machine Learning Approaches with Explainable AI for Multi-Class Intrusion Detection in Drone Networks23 Sep 2025
- 2025SkyVLN: Vision-and-Language Navigation and NMPC Control for UAVs in Urban Environments9 Jul 2025