The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Survey on computational-intelligence-based UAV path planning
Abstract
Abstract The key objective of unmanned aerial vehicle (UAV) path planning is to produce a flight path that connects a start state and a goal state while meeting the required constraints. Computational intelligence (CI) is a set of nature-inspired computational methodologies and approaches for addressing complex real-world problems for which mathematical or traditional modelling does not perform well. It has been applied in the field of UAVs since it can yield effective, accurate and rapid solutions. This article provides an overview of studies on UAV path planning based on CI methods published in major journals and conference proceedings. We survey relevant studies with respect to different CI algorithms utilized in UAV path planning, the types of time domain in UAV path planning, namely, offline and online, and the types of environment models, namely, 2D and 3D. It is observed that CI methods outperform traditional methods on online and 3D problems. The analysis is useful for identifying key results from UAV path planning research and is leveraged in this article to highlight trends and open issues.
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782
Asynchronous Methods for Deep Reinforcement Learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., Silver, D., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1602.01783 · Cited by 7,132
Reinforcement learning in robotics: A survey
Kober, J., Bagnell, J. A., & Peters, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913495721 · Cited by 3,160
Target-driven visual navigation in indoor scenes using deep reinforcement learning
Zhu, Y., Mottaghi, R., Kolve, E., Lim, J. J., Gupta, A., Fei-Fei, L., & Farhadi, A. (2017) · 2017 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2017.7989381 · Cited by 1,533
Model-Free reinforcement learning with continuous action in practice
Degris, T., Pilarski, P. M., & Sutton, R. S. (2012) · 2012 American Control Conference (ACC) · doi:10.1109/acc.2012.6315022 · Cited by 239
Cited by
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 282
UAV Path Planning Using Optimization Approaches: A Survey
Ait Saadi, A., Soukane, A., Meraihi, Y., Benmessaoud Gabis, A., Mirjalili, S., & Ramdane-Cherif, A. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09742-7 · Cited by 269
Metadata
- Authors
- Zhao, Y., Zheng, Z., & Liu, Y.
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 158
- Pages
- 54-64
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W2808115698
- Cited by
- 418
- References
- 6
- FWCI
- 15.95
- Type
- journal article
- Language
- en
- Open access
- closed
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