The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Multiple UAV Systems: A Survey
Open accessAbstract
Nowadays, Unmanned Aerial Vehicles (UAVs) are used in many different applications. Using systems of multiple UAVs is the next obvious step in the process of applying this technology for variety of tasks. There are few research works that cover the applications of these systems and they are all highly specialized. The goal of this survey is to fill this gap by providing a generic review on different applications of multiple UAV systems that have been developed in recent years. We also present a nomenclature and architecture taxonomy for these systems. In the end, a discussion on current trends and challenges is provided.
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Citations
Cites
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472 · cited 1×
“Examples of key points extractors which are traditionally used include the scale-invariant feature transform (SIFT) [98], the speeded up robust features (SURF) [99], and Harris operator or Harris corner detector [100].”
MethodSURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955 · cited 1×
“Examples of key points extractors which are traditionally used include the scale-invariant feature transform (SIFT) [98], the speeded up robust features (SURF) [99], and Harris operator or Harris corner detector [100].”
MethodA Combined Corner and Edge Detector
Harris, C., & Stephens, M. (1988) · Procedings of the Alvey Vision Conference 1988 · doi:10.5244/c.2.23 · Cited by 12,901
ORB-SLAM: A Versatile and Accurate Monocular SLAM System
Mur-Artal, R., Montiel, J. M. M., & Tardos, J. D. (2015) · IEEE Transactions on Robotics · doi:10.1109/tro.2015.2463671 · Cited by 6,778
A model for types and levels of human interaction with automation
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/3468.844354 · Cited by 4,195 · cited 2×
“In [118], the following classi fi cation of levels of autonomy was provided: When speaking particularly about multiple UAV system autonomy, by the time when the current paper was written, to the best of our knowledge, there were no systems that would exceed level 5 of the presented classi fi cation.”
Method
Cited by
Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
Mohsan, S. A. H., Khan, M. A., Noor, F., Ullah, I., & Alsharif, M. H. (2022) · Drones · doi:10.3390/drones6060147 · Cited by 916
A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges
McEnroe, P., Wang, S., & Liyanage, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3176400 · Cited by 482
Metadata
- Authors
- Skorobogatov, G., Barrado, C., & Salamí, E.
- Venue
- Unmanned Systems
- Publisher
- World Scientific Pub Co Pte Ltd
- Published
- 2020-04
- Volume
- 08
- Issue
- 02
- Pages
- 149-169
- ISSN
- 2301-3850 (ISSN-L)
- 2301-3869
- OpenAlex
- W2987256267
- Cited by
- 261
- References
- 5
- FWCI
- 13.63
- Type
- journal article
- Language
- en
- Open access
- green
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