WFRF:(Zhao Nan)
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- Wang, ShuqiCollege of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China; National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China (författare)
Trajectory Planning for UAV-Assisted Data Collection in IoT Network: A Double Deep Q Network Approach
- Artikel/kapitelEngelska2024
Förlag, utgivningsår, omfång ...
- Multidisciplinary Digital Publishing Institute (MDPI),2024
- printrdacarrier
Nummerbeteckningar
- LIBRIS-ID:oai:DiVA.org:kth-346400
- https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-346400URI
- https://doi.org/10.3390/electronics13081592DOI
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- Språk:engelska
- Sammanfattning på:engelska
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Klassifikation
Anmärkningar
- QC 20240516
- Unmanned aerial vehicles (UAVs) are becoming increasingly valuable as a new type of mobile communication device and autonomous decision-making device in many application areas, including the Internet of Things (IoT). UAVs have advantages over other stationary devices in terms of high flexibility. However, a UAV, as a mobile device, still faces some challenges in optimizing its trajectory for data collection. Firstly, the high complexity of the movement action and state space of the UAV’s 3D trajectory is not negligible. Secondly, in unknown urban environments, a UAV must avoid obstacles accurately in order to ensure a safe flight. Furthermore, without a priori wireless channel characterization and ground device locations, a UAV must reliably and safely complete the data collection from the ground devices under the threat of unknown interference. All of these require the proposing of intelligent and automatic onboard trajectory optimization techniques. This paper transforms the trajectory optimization problem into a Markov decision process (MDP), and deep reinforcement learning (DRL) is applied to the data collection scenario. Specifically, the double deep Q-network (DDQN) algorithm is designed to address intelligent UAV trajectory planning that enables energy-efficient and safe data collection. Compared with the traditional algorithm, the DDQN algorithm is much better than the traditional Q-Learning algorithm, and the training time of the network is shorter than that of the deep Q-network (DQN) algorithm.
Ämnesord och genrebeteckningar
- NATURVETENSKAP Data- och informationsvetenskap Datavetenskap hsv//swe
- NATURAL SCIENCES Computer and Information Sciences Computer Sciences hsv//eng
- deep reinforcement learning
- double deep Q-network (DDQN)
- trajectory planning
- UAV
Biuppslag (personer, institutioner, konferenser, titlar ...)
- Qi, NanCollege of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China; National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China (författare)
- Jiang, HuaCollege of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China; National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China (författare)
- Xiao, Ming,1975-KTH,Teknisk informationsvetenskap(Swepub:kth)u1iq6n9a (författare)
- Liu, HaoxuanCollege of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China; National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China (författare)
- Jia, LuliangSchool of Space Information, Space Engineering University, Beijing 101416, China (författare)
- Zhao, DanCollege of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China (författare)
- College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China; National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, ChinaTeknisk informationsvetenskap (creator_code:org_t)
Sammanhörande titlar
- Ingår i:Electronics: Multidisciplinary Digital Publishing Institute (MDPI)13:82079-9292
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