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Deep Reinforcement Learning-Based UAV Path Planning Algorithm in Agricultural Time-Constrained Data CollectionCAI, M.   , FAN, S. , XIAO, G. , HU, K. |
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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
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. |
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
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Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
