The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Evolutionary algorithm based offline/online path planner for uav navigation
Abstract
An evolutionary algorithm based framework, a combination of modified breeder genetic algorithms incorporating characteristics of classic genetic algorithms, is utilized to design an offline/online path planner for unmanned aerial vehicles (UAVs) autonomous navigation. The path planner calculates a curved path line with desired characteristics in a three-dimensional (3-D) rough terrain environment, represented using B-spline curves, with the coordinates of its control points being the evolutionary algorithm artificial chromosome genes. Given a 3-D rough environment and assuming flight envelope restrictions, two problems are solved: i) UAV navigation using an offline planner in a known environment, and, ii) UAV navigation using an online planner in a completely unknown environment. The offline planner produces a single B-Spline curve that connects the starting and target points with a predefined initial direction. The online planner, based on the offline one, is given on-board radar readings which gradually produces a smooth 3-D trajectory aiming at reaching a predetermined target in an unknown environment; the produced trajectory consists of smaller B-spline curves smoothly connected with each other. Both planners have been tested under different scenarios, and they have been proven effective in guiding an UAV to its final destination, providing near-optimal curved paths quickly and efficiently.
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Citations
Cites
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Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond
Zeng, Y., Wu, Q., & Zhang, R. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2952892 · Cited by 1,589
Otto, A., Agatz, N., Campbell, J., Golden, B., & Pesch, E. (2018) · Networks · doi:10.1002/net.21818 · Cited by 985
“Thus, we do not include articles on obstacle-avoiding path planning from a given starting point to a given end point [12, 63, 108, 153, 194, 203, 220, 227, 245, 306] or on planning trajectories of drones flying in formation [220].”
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Airborne Communication Networks: A Survey
Cao, X., Yang, P., Alzenad, M., Xi, X., Wu, D., & Yanikomeroglu, H. (2018) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2018.2864423 · Cited by 331
Energy-Efficient Data Collection and Device Positioning in UAV-Assisted IoT
Wang, Z., Liu, R., Liu, Q., Thompson, J. S., & Kadoch, M. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2952364 · Cited by 308
Metadata
- Authors
- Nikolos, I. K., Valavanis, K. P., Tsourveloudis, N. C., & Kostaras, A. N.
- Venue
- IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics)
- Published
- 2003-12
- Volume
- 33
- Issue
- 6
- Pages
- 898-912
- ISSN
- 1083-4419 (ISSN-L)
- OpenAlex
- W2130724591
- Cited by
- 508
- References
- 11
- FWCI
- 9.11
- Type
- journal article
- Language
- en
- Open access
- closed
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