An improved dung beetle optimizer for UAV 3D path planning
Author:
Funder
Science Popularization Enhancement Project of the China Association for Science and Technology
LiaoNing Revitalization Talents Program
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11227-024-06414-0.pdf
Reference35 articles.
1. Song F, Deng M, Xing H, Liu Y, Ye F, Xiao Z (2024) Energy-efficient trajectory optimization with wireless charging in UAV-assisted MEC based on multi-objective reinforcement learning. IEEE Trans Mob Comput. https://doi.org/10.1109/TMC.2024.3384405
2. Song F, Yang Q, Deng M, Xing H, Liu Y, Yu X, Li K, Xu L (2024) AoI and energy tradeoff for aerial-ground collaborative MEC: a multi-objective learning approach. IEEE Trans Mob Comput. https://doi.org/10.1109/TMC.2024.3394568
3. Hu Y, Yao Y, Ren Q, Zhou X (2020) 3D multi-UAV cooperative velocity-aware motion planning. Future Gener Comput Syst 102:762–774. https://doi.org/10.1016/j.future.2019.09.030
4. Hu Y, Zhou X, Yao Y (2019) Decentralized velocity-aware motion planning for multi-agent coordination. In: Proceedings of the 2019 IEEE international conference on service-oriented system engineering (SOSE)
5. Chowdhury A, De D (2023) RGSO-UAV: reverse glowworm swarm optimization inspired UAV path-planning in a 3D dynamic environment. Ad Hoc Netw 140:103068. https://doi.org/10.1016/j.adhoc.2022.103068
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