mlsanityData to 14 Oct 2026

Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey

arXiv 2211.04324 ↗First posted 7 Nov 2022cs.RO cs.CV cs.LGcited by 28

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Abstract (arXiv)

The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications and identify the potential growth areas and research gaps. We emphasise the four key components of UAV operations and communications to which ML can significantly contribute, namely, perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. We classify the latest popular ML tools based on their applications to the four components and conduct gap analyses. This survey also takes a step forward by pointing out significant challenges in the upcoming realm of ML-aided automated UAV operations and communications. It is revealed that different ML techniques dominate the applications to the four key modules of UAV operations and communications. While there is an increasing trend of cross-module designs, little effort has been devoted to an end-to-end ML framework, from perception and feature extraction to aerodynamic control and operation. It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before full automation of UAVs and potential cooperation between UAVs and humans come to fruition.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). Builds on 70 of its 358 references that are in the map. First posted on arXiv 7 Nov 2022.

Builds on

70 of 358 references.

References · 70

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2022Generative Adversarial Networkscited by 5,627
  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  4. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
  5. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  6. 2017Multimodal Machine Learning: A Survey and Taxonomycited by 1,013
  7. 2016End to End Learning for Self-Driving Carscited by 1,009
  8. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  9. 2016Simple Online and Realtime Trackingcited by 868
  10. 2020The Future of Digital Health with Federated Learningcited by 633
  11. 2015An Introduction to Convolutional Neural Networkscited by 514
  12. 2013OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networkscited by 224
38 more
  1. 2020FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understandingcited by 181
  2. 2018Applications of Deep Reinforcement Learning in Communications and Networking: A Surveycited by 167
  3. 2020Federated Learning in Vehicular Networkscited by 53
  4. 2018Reinforcement Learning for UAV Attitude Controlcited by 48
  5. 2020On Episodes, Prototypical Networks, and Few-shot Learningcited by 46
  6. 2020Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differencescited by 40
  7. 2018Machine learning for Internet of Things data analysis: A surveycited by 29
  8. 2019Accessing From The Sky: A Tutorial on UAV Communications for 5G and Beyondcited by 28
  9. 2020Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV with Deep Reinforcement Learningcited by 27
  10. 2018Memory-based Deep Reinforcement Learning for Obstacle Avoidance in UAV with Limited Environment Knowledgecited by 26
  11. 2019UAV Communications for 5G and Beyond: Recent Advances and Future Trendscited by 25
  12. 2020Multi-Agent Deep Reinforcement Learning Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computingcited by 24
  13. 2016Survey of Important Issues in UAV Communication Networkscited by 23
  14. 2021Time-Optimal Planning for Quadrotor Waypoint Flightcited by 23
  15. 2020Path Planning for UAV-Mounted Mobile Edge Computing with Deep Reinforcement Learningcited by 21
  16. 2020Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applicationscited by 21
  17. 2018Survey on UAV Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challengescited by 20
  18. 2019Learning Visuomotor Policies for Aerial Navigation Using Cross-Modal Representationscited by 19
  19. 2019Deep Reinforcement Learning for Minimizing Age-of-Information in UAV-assisted Networkscited by 13
  20. 2020Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimizationcited by 13
  21. 2018Distributed Cooperative Spectrum Sharing in UAV Networks Using Multi-Agent Reinforcement Learningcited by 12
  22. 2018A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehiclescited by 11
  23. 2019On-board Deep Q-Network for UAV-assisted Online Power Transfer and Data Collectioncited by 11
  24. 2019Parameter Optimization and Learning in a Spiking Neural Network for UAV Obstacle Avoidance targeting Neuromorphic Processorscited by 11
  25. 2020Artificial Intelligence for UAV-enabled Wireless Networks: A Surveycited by 11
  26. 2018A Survey of Channel Modeling for UAV Communicationscited by 9
  27. 2020Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approachcited by 9
  28. 2018DroNet: Efficient convolutional neural network detector for real-time UAV applicationscited by 8
  29. 2019Rule Extraction in Unsupervised Anomaly Detection for Model Explainability: Application to OneClass SVMcited by 8
  30. 2020Meta-Reinforcement Learning for Trajectory Design in Wireless UAV Networkscited by 8
  31. 2021Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networkscited by 8
  32. 2018Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey, and Future Directionscited by 7
  33. 2016Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planningcited by 6
  34. 2018Machine Learning for Wireless Connectivity and Security of Cellular-Connected UAVscited by 6
  35. 2018UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimizationcited by 6
  36. 2018Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspectivecited by 5
  37. 2020Artificial Intelligence Aided Next-Generation Networks Relying on UAVscited by 5
  38. 2020UAV-to-Device Underlay Communications: Age of Information Minimization by Multi-agent Deep Reinforcement Learningcited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 6; 2025: 11; 2026: 8.

28 papers.

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  1. 2024Large Language Models for UAVs: Current State and Pathways to the Futurecited by 33
  2. 2024Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunitiescited by 14
  3. 2025SkyVLN: Vision-and-Language Navigation and NMPC Control for UAVs in Urban Environmentscited by 14
  4. 2025LLM-Enabled In-Context Learning for Data Collection Scheduling in UAV-assisted Sensor Networkscited by 12
  5. 2025From Prompts to Protection: Large Language Model-Enabled In-Context Learning for Smart Public Safety UAVcited by 10
  6. 2025Next-Generation LLM for UAV: From Natural Language to Autonomous Flightcited by 10
  7. 2025Diffusion Models for Smarter UAVs: Decision-Making and Modelingcited by 7
  8. 2024Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunitiescited by 6
  9. 2023Deep Reinforcement Learning for Joint Cruise Control and Intelligent Data Acquisition in UAVs-Assisted Sensor Networkscited by 5
  10. 2026Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorialcited by 4
  11. 2023Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement Learningcited by 2
  12. 2023Autonomous Advanced Aerial Mobility -- An End-to-end Autonomy Framework for UAVs and Beyondcited by 2
16 more
  1. 2024Fusion Flow-enhanced Graph Pooling Residual Networks for Unmanned Aerial Vehicles Surveillance in Day and Night Dual Visionscited by 2
  2. 2025Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learningcited by 2
  3. 2025AD-Det: Boosting Object Detection in UAV Images with Focused Small Objects and Balanced Tail Classescited by 2
  4. 2025Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approachescited by 2
  5. 2025Adversarial Reinforcement Learning for Robust Control of Fixed-Wing Aircraft under Model Uncertaintycited by 1
  6. 2025Bridging Earth and Space: A Survey on HAPS for Non-Terrestrial Networkscited by 1
  7. 2024Autonomous Strike UAVs for Counterterrorism Missions: Challenges and Preliminary Solutionscited by 0
  8. 2024Scheduling Drone and Mobile Charger via Hybrid-Action Deep Reinforcement Learningcited by 0
  9. 2025Advancing Autonomous Emergency Response Systems: A Generative AI Perspectivecited by 0
  10. 2026Bridging Network Fragmentation: A Semantic-Augmented DRL Framework for UAV-aided VANETscited by 0
  11. 2026Cyber Deception for Mission Surveillance via Hypergame-Theoretic Deep Reinforcement Learningcited by 0
  12. 2026Dynamic Mask Enhanced Intelligent Multi-UAV Deployment for Urban Vehicular Networkscited by 0
  13. 2026Learning Families of Fixed-Wing Aircraft Control Policies with Hypernetwork-Conditioned Reinforcement Learningcited by 0
  14. 2026SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Searchcited by 0
  15. 2026LLM-Centric Agentic AI for UAV Swarms: Architecture, Enabling Technologies, and Open Problemscited by 0
  16. 2026LAPF: LLM-Agent-Based Path Finder Using the UAVScenes Datasetcited by 0