The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
A novel reinforcement learning based grey wolf optimizer algorithm for unmanned aerial vehicles (UAVs) path planning
Abstract
Abstract Unmanned aerial vehicles (UAVs) have been used in wide range of areas, and a high-quality path planning method is needed for UAVs to satisfy their applications. However, many algorithms reported in the literature may not feasible or efficient, especially in the face of three-dimensional complex flight environment. In this paper, a novel reinforcement learning based grey wolf optimizer algorithm called RLGWO has been presented for solving this problem. In the proposed algorithm, the reinforcement learning is inserted that the individual is controlled to switch operations adaptively according to the accumulated performance. Considering that the proposed algorithm is designed to serve for UAVs path planning, four operations have been introduced for each individual: exploration, exploitation, geometric adjustment, and optimal adjustment. In addition, the cubic B-spline curve is used to smooth the generated flight route and make the planning path be suitable for the UAVs. The simulation experimental results show that the RLGWO algorithm can acquire a feasible and effective route successfully in complicated environment.
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Self-citationBinary grey wolf optimization approaches for feature selection
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Cited by
Drone Deep Reinforcement Learning: A Review
Azar, A. T., Koubaa, A., Ali Mohamed, N., Ibrahim, H. A., Ibrahim, Z. F., Kazim, M., Ammar, A., Benjdira, B., Khamis, A. M., Hameed, I. A., et al. (2021) · Electronics · doi:10.3390/electronics10090999 · Cited by 314
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
Optimization Methods Applied to Motion Planning of Unmanned Aerial Vehicles: A Review
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Rezoug, A., Iqbal, J., & Nemra, A. (2025) · Systems Science & Control Engineering · doi:10.1080/21642583.2024.2449156 · Cited by 3
Reliable and Efficient Data Collection in UAV-based IoT Networks
Joshi, P., Kalita, A., & Gurusamy, M. (2023) · arXiv · doi:10.48550/arxiv.2311.05303 · Cited by 61
“Through concerted efforts in these areas, researchers and industry stakeholders can overcome current limitations and pave the way for widespread BVLOS deployment of UAVs across various applications.”
Metadata
- Authors
- Qu, C., Gai, W., Zhong, M., & Zhang, J.
- Venue
- Applied Soft Computing
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 89
- Pages
- 106099
- ISSN
- 1568-4946 (ISSN-L)
- OpenAlex
- W3003100621
- Cited by
- 334
- References
- 29
- FWCI
- 15.25
- Type
- journal article
- Language
- en
- Open access
- closed
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- Qu, C., Gai, W., Zhong, M., & Zhang, J. (2020). A novel reinforcement learning based grey wolf optimizer algorithm for unmanned aerial vehicles (UAVs) path planning. Applied Soft Computing. https://doi.org/10.1016/j.asoc.2020.106099
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