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University of Suceava
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Print ISSN: 1582-7445
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  2/2023 - 12
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Deep Reinforcement Learning-Based UAV Path Planning Algorithm in Agricultural Time-Constrained Data Collection

CAI, M. See more information about CAI, M. on SCOPUS See more information about CAI, M. on IEEExplore See more information about CAI, M. on Web of Science, FAN, S. See more information about  FAN, S. on SCOPUS See more information about  FAN, S. on SCOPUS See more information about FAN, S. on Web of Science, XIAO, G. See more information about  XIAO, G. on SCOPUS See more information about  XIAO, G. on SCOPUS See more information about XIAO, G. on Web of Science, HU, K. See more information about HU, K. on SCOPUS See more information about HU, K. on SCOPUS See more information about HU, K. on Web of Science
 
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Download PDF pdficon (1,786 KB) | Citation | Downloads: 352 | Views: 486

Author keywords
adaptive exploration, deep reinforcement learning, Markov decision process, path planning, reward function

References keywords
data(10), learning(9), internet(9), communications(9), collection(9), time(8), control(8), system(7), reinforcement(7), networks(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2023-05-31
Volume 23, Issue 2, Year 2023, On page(s): 101 - 108
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2023.02012
Web of Science Accession Number: 001009953400012
SCOPUS ID: 85164343239

Abstract
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In the Agricultural Internet of Things (AgIoT), Unmanned Aerial Vehicles (UAVs) can be used to collect sensor data. Thus, UAVs must plan the appropriate data collection paths so that sensors can collect the data under different positions and generate time-constrained data. Therefore, this paper proposes a UAV path planning algorithm based on Deep Reinforcement Learning (DRL), which jointly optimizes location, energy, and time deadline to maximize the data-energy ratio. The path planning process is modeled and decomposed into a Markov Decision Process (MDP), and then a Prioritized Experience Replay Double Deep Q Network (PER-DDQN) model is used to calculate the optimal solution. Furthermore, a time-constrained reward function and an improved adaptive upper confidence bound (UCB) exploration function are proposed to balance exploration and exploitation in the DRL algorithm, affording the developed algorithm to converge quickly and smoothly. The simulations demonstrate that compared with traditional methods, the proposed algorithm presents better path selection during the data collection process, lower execution time, and a higher data-energy ratio. Our algorithm promotes the use of UAV in AgIoT.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 23,954 TCR
SCOPUS® Citations for all references: 31,499 TCR

Web of Science® Average Citations per reference: 749 ACR
SCOPUS® Average Citations per reference: 984 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-07-15 04:58 in 203 seconds.




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