The DOI matches its Crossref record; 100 of the 100 references it cites were found in a registry or an index.
UAV Swarm Intelligence: Recent Advances and Future Trends
Open accessAbstract
The dynamic uncertain environment and complex tasks determine that the unmanned aerial vehicle (UAV) system is bound to develop towards clustering, autonomy, and intelligence. In this article, we present a comprehensive survey of UAV swarm intelligence from the hierarchical framework perspective. Firstly, we review the basics and advances of UAV swarm intelligent technology. Then we look inside to investigate the research work by classifying UAV swarm intelligence research into five layers, i.e., decision-making layer, path planning layer, control layer, communication layer, and application layer. Furthermore, the relationship between each level is explicitly illustrated, and the research trends of each layer are given. Finally, limitations and possible technology trends of swarm intelligence are also covered to enable further research interests. Through this in-depth literature review, we intend to provide novel insights into the latest technologies in UAV swarm intelligence.
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Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192
Particle swarm optimization: An overview
Poli, R., Kennedy, J., & Blackwell, T. (2007) · Swarm Intelligence · doi:10.1007/s11721-007-0002-0 · Cited by 21,101
On the performance of artificial bee colony (ABC) algorithm
Karaboga, D., & Basturk, B. (2008) · Applied Soft Computing · doi:10.1016/j.asoc.2007.05.007 · Cited by 3,672
Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges
Shakhatreh, H., Sawalmeh, A. H., Al-Fuqaha, A., Dou, Z., Almaita, E., Khalil, I., Othman, N. S., Khreishah, A., & Guizani, M. (2019) · IEEE Access · doi:10.1109/access.2019.2909530 · Cited by 2,529
(2003) · Selected Papers of Alan J Hoffman · doi:10.1142/9789812796936_others05 · Cited by 2,465
The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization
(1986) · Journal of the Operational Research Society · doi:10.1057/jors.1986.93 · Cited by 1,679
Survey on Unmanned Aerial Vehicle Networks for Civil Applications: A Communications Viewpoint
Hayat, S., Yanmaz, E., & Muzaffar, R. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2560343 · Cited by 1,386
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
Foerster, J. N., Assael, Y. M., de Freitas, N., & Whiteson, S. (2016) · arXiv · doi:10.48550/arxiv.1605.06676 · Cited by 1,217
Product lifecycle management: the new paradigm for enterprises
Grieves, M. W. (2005) · International Journal of Product Development · doi:10.1504/ijpd.2005.006669 · Cited by 741
Designing Collective Behavior in a Termite-Inspired Robot Construction Team
Werfel, J., Petersen, K., & Nagpal, R. (2014) · Science · doi:10.1126/science.1245842 · Cited by 599
Cited by
Networked Unmanned Aerial Vehicles for Surveillance and Monitoring: A Survey
Li, X., & Savkin, A. V. (2021) · Future Internet · doi:10.3390/fi13070174 · Cited by 107
Application of Deep Reinforcement Learning to UAV Swarming for Ground Surveillance
Arranz, R., Carramiñana, D., Miguel, G. D., Besada, J. A., & Bernardos, A. M. (2023) · Sensors · doi:10.3390/s23218766 · Cited by 58
Metadata
- Authors
- Zhou, Y., Rao, B., & Wang, W.
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 183856-183878
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3092163236
- Cited by
- 373
- References
- 100
- FWCI
- 13.20
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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