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المؤلفLi, Kai
المؤلفNi, Wei
المؤلفTovar, Eduardo
المؤلفGuizani, Mohsen
تاريخ الإتاحة2022-12-05T15:06:15Z
تاريخ النشر2020-06-01
اسم المنشور2020 International Wireless Communications and Mobile Computing, IWCMC 2020
المعرّفhttp://dx.doi.org/10.1109/IWCMC48107.2020.9148316
الاقتباسLi, K., Ni, W., Tovar, E., & Guizani, M. (2020, June). Deep reinforcement learning for real-time trajectory planning in UAV networks. In 2020 International Wireless Communications and Mobile Computing (IWCMC) (pp. 958-963). IEEE.‏
الترقيم الدولي الموحد للكتاب 9781728131290
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85089694273&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/36939
الملخصIn Unmanned Aerial Vehicle (UAV)-enabled wireless powered sensor networks, a UAV can be employed to charge the ground sensors remotely via Wireless Power Transfer (WPT) and collect the sensory data. This paper focuses on trajectory planning of the UAV for aerial data collection and WPT to minimize buffer overflow at the ground sensors and unsuccessful transmission due to lossy airborne channels. Consider network states of battery levels and buffer lengths of the ground sensors, channel conditions, and location of the UAV. A flight trajectory planning optimization is formulated as a Partial Observable Markov Decision Process (POMDP), where the UAV has partial observation of the network states. In practice, the UAV-enabled sensor network contains a large number of network states and actions in POMDP while the up-to-date knowledge of the network states is not available at the UAV. To address these issues, we propose an onboard deep reinforcement learning algorithm to optimize the realtime trajectory planning of the UAV given outdated knowledge on the network states.
راعي المشروعThis work was partially supported by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (UIDB/04234/2020); also by the Operational Competitiveness Programme and Internationalization (COMPETE 2020) under the PT2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by national funds through the FCT, within project(s) POCI-01-0145-FEDER-029074 (ARNET).
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعDeep reinforcement learning
Trajectory planning
Unmanned aerial vehicles
Wireless power transfer
Wireless sensor networks
العنوانDeep Reinforcement Learning for Real-Time Trajectory Planning in UAV Networks
النوعConference Proceedings
الصفحات958-963
dc.accessType Abstract Only


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