# Machine Learning Methods for Management UAV Flocks -- a Survey

arXiv: https://arxiv.org/abs/2108.13448 · HTML: https://mlsanity.com/paper/2108.13448/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 143 references that are in the map.
- First posted on arXiv 30 Aug 2021.

## Builds on (most cited first, 28 of 28)

- [Learning Combinatorial Optimization Algorithms over Graphs](https://mlsanity.com/paper/1704.01665/) (2017)
- [Deep Recurrent Q-Learning for Partially Observable MDPs](https://mlsanity.com/paper/1507.06527/) (2015)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management](https://mlsanity.com/paper/1705.09412/) (2017)
- [Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks](https://mlsanity.com/paper/1810.10408/) (2018)
- [Spatial Deep Learning for Wireless Scheduling](https://mlsanity.com/paper/1808.01486/) (2018)
- [Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms](https://mlsanity.com/paper/2002.08196/) (2020)
- [Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.07574/) (2020)
- [UAV Communications for 5G and Beyond: Recent Advances and Future Trends](https://mlsanity.com/paper/1901.06637/) (2019)
- [A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence](https://mlsanity.com/paper/2010.09317/) (2020)
- [Survey on UAV Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challenges](https://mlsanity.com/paper/1809.01752/) (2018)
- [Trajectory Design and Power Control for Multi-UAV Assisted Wireless Networks: A Machine Learning Approach](https://mlsanity.com/paper/1812.07665/) (2018)
- [Reinforcement Learning in Multiple-UAV Networks: Deployment and Movement Design](https://mlsanity.com/paper/1904.05242/) (2019)
- [Distributed Cooperative Spectrum Sharing in UAV Networks Using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1811.05053/) (2018)
- [Reinforcement Learning for Decentralized Trajectory Design in Cellular UAV Networks with Sense-and-Send Protocol](https://mlsanity.com/paper/1809.02934/) (2018)
- [A Comprehensive Review of Shepherding as a Bio-inspired Swarm-Robotics Guidance Approach](https://mlsanity.com/paper/1912.07796/) (2019)
- [Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey, and Future Directions](https://mlsanity.com/paper/1810.09729/) (2018)
- [Deep Learning for Radio Resource Allocation in Multi-Cell Networks](https://mlsanity.com/paper/1808.00667/) (2018)
- [Reinforcement Learning for a Cellular Internet of UAVs: Protocol Design, Trajectory Control, and Resource Management](https://mlsanity.com/paper/1911.08771/) (2019)
- [Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective](https://mlsanity.com/paper/1812.06821/) (2018)
- [Machine Learning for Predictive On-Demand Deployment of UAVs for Wireless Communications](https://mlsanity.com/paper/1805.00061/) (2018)
- [Ready Player One: UAV Clustering based Multi-Task Offloading for Vehicular VR/AR Gaming](https://mlsanity.com/paper/1904.03861/) (2019)
- [An Extensive Survey on the Internet of Drones](https://mlsanity.com/paper/2007.12611/) (2020)
- [Reinforcement Learning-Based Trajectory Design for the Aerial Base Stations](https://mlsanity.com/paper/1906.09550/) (2019)
- [Control of Cooperative Unmanned Aerial Vehicles: Review of Applications, Challenges, and Algorithms](https://mlsanity.com/paper/1908.02789/) (2019)
- [Placement Optimization of Aerial Base Stations with Deep Reinforcement Learning](https://mlsanity.com/paper/1911.08111/) (2019)
- [Communication and networking technologies for UAVs: A survey](https://mlsanity.com/paper/2009.02280/) (2020)
- [An Overview on Optimal Flocking](https://mlsanity.com/paper/2009.14279/) (2020)

## Built on by (most cited first, 2 of 2)

- [Autonomous Unmanned Aerial Vehicle Navigation using Reinforcement Learning: A Systematic Review](https://mlsanity.com/paper/2208.12328/) (2022)
- [Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics](https://mlsanity.com/paper/2301.00912/) (2023)

## Abstract (arXiv)

The development of unmanned aerial vehicles (UAVs) has been gaining momentum in recent years owing to technological advances and a significant reduction in their cost. UAV technology can be used in a wide range of domains, including communication, agriculture, security, and transportation. It may be useful to group the UAVs into clusters/flocks in certain domains, and various challenges associated with UAV usage can be alleviated by clustering. Several computational challenges arise in UAV flock management, which can be solved by using machine learning (ML) methods. In this survey, we describe the basic terms relating to UAVS and modern ML methods, and we provide an overview of related tutorials and surveys. We subsequently consider the different challenges that appear in UAV flocks. For each issue, we survey several machine learning-based methods that have been suggested in the literature to handle the associated challenges. Thereafter, we describe various open issues in which ML can be applied to solve the different challenges of flocks, and we suggest means of using ML methods for this purpose. This comprehensive review may be useful for both researchers and developers in providing a wide view of various aspects of state-of-the-art ML technologies that are applicable to flock management.