The DOI matches its Crossref record; 82 of the 82 references it cites were found in a registry or an index.
Optimization Methods Applied to Motion Planning of Unmanned Aerial Vehicles: A Review
Open accessAbstract
A system that can fly off and touches down to execute particular tasks is a flying robot. Nowadays, these flying robots are capable of flying without human control and make decisions according to the situation with the help of onboard sensors and controllers. Among flying robots, Unmanned Aerial Vehicles (UAVs) are highly attractive and applicable for military and civilian purposes. These applications require motion planning of UAVs along with collision avoidance protocols to get better robustness and a faster convergence rate to meet the target. Further, the optimization algorithm improves the performance of the system and minimizes the convergence error. In this survey, diverse scholarly articles were gathered to highlight the motion planning for UAVs that use bio-inspired algorithms. This study will assist researchers in understanding the latest work done in the motion planning of UAVs through various optimization techniques. Moreover, this review presents the contributions and limitations of every article to show the effectiveness of the proposed work.
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Citations
Cites
Optimal and Efficient Path Planning for Partially Known Environments
Stentz, A. (1997) · Springer eBooks · doi:10.1007/978-1-4615-6325-9_11 · Cited by 1,240
Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges
Aggarwal, S., & Kumar, N. (2019) · Computer Communications · doi:10.1016/j.comcom.2019.10.014 · Cited by 1,025
Safety-enhanced UAV path planning with spherical vector-based particle swarm optimization
Phung, M. D., & Ha, Q. P. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107376 · Cited by 603
A survey on vision-based UAV navigation
Lu, Y., Xue, Z., Xia, G.-S., & Zhang, L. (2018) · Geo-spatial Information Science · doi:10.1080/10095020.2017.1420509 · Cited by 480
Efficient path planning for UAV formation via comprehensively improved particle swarm optimization
Shao, S., Peng, Y., He, C., & Du, Y. (2019) · ISA Transactions · doi:10.1016/j.isatra.2019.08.018 · Cited by 379
Bortoff, S. A. (2000) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2000.878915 · Cited by 355
Qu, C., Gai, W., Zhong, M., & Zhang, J. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106099 · Cited by 347
Collision Avoidance for Cooperative UAVs With Optimized Artificial Potential Field Algorithm
Sun, J., Tang, J., & Lao, S. (2017) · IEEE Access · doi:10.1109/access.2017.2746752 · Cited by 325
Energy-Efficient Data Collection and Device Positioning in UAV-Assisted IoT
Wang, Z., Liu, R., Liu, Q., Thompson, J., & Kadoch, M. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2952364 · Cited by 310
A novel hybrid grey wolf optimizer algorithm for unmanned aerial vehicle (UAV) path planning
Qu, C., Gai, W., Zhang, J., & Zhong, M. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105530 · Cited by 295
Research works printed but not linked: 5
- Ouns, B., Abrassart, A., Garcia, F., and Larrieu, N. (2014, January 27–30). A mobility model for UAV ad hoc network. Proceedings of the 2014 International Conference on Unmanned Aircraft Systems (ICUAS), Orlando, FL, USA.
- Kuntz, R.R., Kienitz, K.H., and Brandão, M.P. (2011, January 5–12). Development of a multi-purpose portable electrical UAV system, fixed & rotative wing. Proceedings of the 2011 Aerospace Conference, Big Sky, MT, USA.
- Iztok, F., Yang, X., Fister, I., Brest, J., and Fister, D. (2013). A brief review of nature-inspired algorithms for optimization. arXiv.
- Beheshti, 2013, A review of population-based meta-heuristic algorithms, Int. J. Adv. Soft Comput. Appl., 5, 1
- Zong, 2017, Research status and development of multi UAV coordinated formation flight control, J. Harbin Inst. Technol., 49, 1
Cited by
Sorted by citations
UAV swarms: research, challenges, and future directions
Alqudsi, Y. S., & Makaracı, M. (2025) · Journal of Engineering and Applied Science · doi:10.1186/s44147-025-00582-3 · Cited by 233
Operational Research: methods and applications
Petropoulos, F., Gilbert Laporte, A., Aktaş, E., Alumur, S. A., Archetti, C., Ayhan, H., Battarra, M., Bennell, J. A., Bourjolly, J.-M., Boylan, J. E., et al. (2023) · Journal of the Operational Research Society · doi:10.1080/01605682.2023.2253852 · Cited by 161
Jahani, H., Khosravi, Y., Kargar, B., Ong, K., & Asian, S. (2024) · International Journal of Production Research · doi:10.1080/00207543.2024.2373425 · Cited by 119
Bio-Inspired Optimization-Based Path Planning Algorithms in Unmanned Aerial Vehicles: A Survey
Poudel, S., Arafat, M. Y., & Moh, S. (2023) · Sensors · doi:10.3390/s23063051 · Cited by 109
Darvishpoor, S., Darvishpour, A., Escarcega, M. A., & Hassanalian, M. (2023) · Drones · doi:10.3390/drones7070427 · Cited by 95
Metadata
- Authors
- Israr, A., Ali, Z. A., Alkhammash, E. H., & Jussila, J. J.
- Affiliations
- Sir Syed University of Engineering and Technology (PK)
- Taif University (SA)
- Häme University of Applied Sciences (FI)
- Corresponding author
- Ali, Zain Anwar
- Venue
- Drones
- Publisher
- MDPI AG
- Published
- 2022-05-13
- Volume
- 6
- Issue
- 5
- Pages
- 126
- ISSN
- 2504-446X (ISSN-L)
- OpenAlex
- W4280496503
- Cited by
- 61
- References
- 82
- Citations by year
- 2026: 4 · 2025: 13 · 2024: 18 · 2023: 22 · 2022: 4
- FWCI
- 1.21
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Drones (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Regional Development project Green Smart Services in Developing Circular Economy SMEs (A77472)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robotic Path Planning Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Engineering › Aerospace Engineering › Air Traffic Management and Optimization
- Keywords
- unmanned aerial vehicles, motion planning, optimization algorithms, bio-inspired algorithms, collision avoidance, flying robots, convergence rate, UAV path planning
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