Enhancing Exploration Efficiency of Fixed-Wing UAVs Through Intelligent Decision-Making and Advanced Control Integration
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3340-8_21
Reference12 articles.
1. Zheng, J., Ding, M., Sun, L., Liu, H.: Distributed stochastic algorithm based on enhanced genetic algorithm for path planning of multi-UAV cooperative area search. IEEE Trans. Intell. Transp. Syst. 24(8), 8290–8303 (2023)
2. Liu, D., Bao, W., Zhu, X., Fei, B., Men, T., Xiao, Z.: Cooperative path optimization for multiple UAVs surveillance in uncertain Eevironment. IEEE Internet Things J. 9(13), 10676–10692 (2022)
3. Pei, M., An, H., Liu, B., Wang, C.: An improved dyna-q algorithm for mobile robot path planning in unknown dynamic environment. IEEE Trans. Systems Man Cybernet. Syst. 52(7), 4415–4425 (2022)
4. Yan, C., Wang, C., Xiang, X., Lan, Z., Jiang, Y.: Deep reinforcement learning of collision-free flocking policies for multiple fixed-wing UAVs using local situation maps. IEEE Trans. Industr. Inf. 18(2), 1260–1270 (2022)
5. Tu, G.-T., Juang, J. -G.: Path planning and obstacle avoidance based on reinforcement learning for UAV application. In: 2021 International Conference on System Science and Engineering, pp. 352–355 (Aug 2021)
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