CLC number: TP183; TP393.1
On-line Access: 2021-05-17
Received: 2019-12-19
Revision Accepted: 2020-06-27
Crosschecked: 2020-10-20
Cited: 0
Clicked: 4519
Citations: Bibtex RefMan EndNote GB/T7714
Wei Li, Bowei Yang, Guanghua Song, Xiaohong Jiang. Dynamic value iteration networks for the planning of rapidly changing UAV swarms[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.1900712 @article{title="Dynamic value iteration networks for the planning of rapidly changing UAV swarms", %0 Journal Article TY - JOUR
用于规划快速变化无人机群的动态值迭代网络1浙江大学航空航天学院,中国杭州市,310027 2浙江大学计算机科学与技术学院,中国杭州市,310027 摘要:在无人机自组网(UANET)中,稀疏且高速移动的无人机节点会动态改变无人机自组网的拓扑结构,这可能会导致无人机自组网服务性能问题。为规划快速变化的无人机群,本文提出一种动态值迭代网络(DVIN)模型,该模型利用无人机自组网的连接信息,采用场景式Q学习方法训练,生成状态值传播函数,使无人机节点能够自适应调节至新的物理位置。然后,评估了动态值迭代网络模型的性能,并将其与非支配排序遗传算法NSGA-II和穷举法比较。仿真结果表明,动态值迭代网络模型显著缩短了无人机节点路径规划的决策时间,且平均成功率更高。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
Reference[1]Abadi M, Barham P, Chen JM, et al., 2016. TensorFlow: a system for large-scale machine learning. Proc 12th USENIX Conf on Operating Systems Design and Implementation, p.265-283. [2]Bekmezci I, Sahingoz OK, Temel Ş, 2013. Flying ad-hoc networks (FANETs): a survey. Ad Hoc Netw, 11(3):1254-1270. [3]Bellman R, 1966. Dynamic programming. Science, 153(3731):34-37. [4]Bertsekas DP, 1995. Dynamic Programming and Optimal Control. Athena Scientific, Belmont, USA. [5]Boureau YL, Bach F, LeCun Y, et al., 2010. Learning mid-level features for recognition. Proc IEEE Computer Society Conf on Computer Vision and Pattern Recognition, p.2559-2566. [6]Buck I, Foley T, Horn D, et al., 2004. Brook for GPUs: stream computing on graphics hardware. ACM Trans Graph, 23(3):777-786. [7]Challita U, Saad W, Bettstetter C, 2018. Deep reinforcement learning for interference-aware path planning of cellular-connected UAVs. Proc IEEE Int Conf on Communications, p.1-7. [8]Cruz F, Wüppen P, Fazrie A, et al., 2019. Action selection methods in a robotic reinforcement learning scenario. Proc IEEE Latin American Conf on Computational Intelligence, p.1-6. [9]Deb K, Pratap A, Agarwal S, et al., 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput, 6(2):182-197. [10]Fontes RR, 2019. Emulando Redes Sem Fio Com Mininet-WiFi. https://github.com/ramonfontes/mn-wifi-book-pt/blob/master/preview-book.pdf [11]Fontes RR, Afzal S, Brito SHB, et al., 2015. Mininet-WiFi: emulating software-defined wireless networks. Proc 11th Int Conf on Network and Service Management, p.384-389. [12]François-Lavet V, Henderson P, Islam R, et al., 2018. An introduction to deep reinforcement learning. Found Trends® Mach Learn, 11(3-4):219-354. [13]Koohifar F, Kumbhar A, Guvenc I, 2017. Receding horizon multi-UAV cooperative tracking of moving RF source. IEEE Commun Lett, 21(6):1433-1436. [14]Krizhevsky A, Sutskever I, Hinton GE, 2017. ImageNet classification with deep convolutional neural networks. Commun ACM, 60(6):84-90. [15]Lee J, Kang BY, Kim DW, 2013. Fast genetic algorithm for robot path planning. Electron Lett, 49(23):1449-1451. [16]Mnih V, Kavukcuoglu K, Silver D, et al., 2015. Human-level control through deep reinforcement learning. Nature, 518(7540):529-533. [17]Mnih V, Badia AP, Mirza L, et al., 2016. Asynchronous methods for deep reinforcement learning. Proc 33rd Int Conf on Machine Learning, p.1928-1937. [18]Niu SF, Chen SH, Guo HY, et al., 2018. Generalized value iteration networks: life beyond lattices. Proc 32nd AAAI Conf on Artificial Intelligence, p.6246-6253. [19]Roberge V, Tarbouchi M, Labonte G, 2013. Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Trans Ind Inform, 9(1):132-141. [20]Schaal S, 1999. Is imitation learning the route to humanoid robots? Trends Cogn Sci, 3(6):233-242. [21]Tamar A, Wu Y, Thomas G, et al., 2017. Value iteration networks. Proc 26th Int Joint Conf on Artificial Intelligence, p.4949-4953. [22]Tokic M, Palm G, 2011. Value-difference based exploration: adaptive control between epsilon-greedy and softmax. Proc 34th Annual German Conf on Advances in Artificial Intelligence, p.335-346. [23]Watkins CJCH, Dayan P, 1992. Q-learning. Mach Learn, 8(3-4):279-292. [24]Zhang CY, Patras P, Haddadi H, 2019. Deep learning in mobile and wireless networking: a survey. IEEE Commun Surv Tutor, 21(3):2224-2287. [25]Zhang T, Li Q, Zhang CS, et al., 2017. Current trends in the development of intelligent unmanned autonomous systems. Front Inform Technol Electron Eng, 18(1):68-85. Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou
310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE |
Open peer comments: Debate/Discuss/Question/Opinion
<1>