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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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| [1] P. Tokekar, J. V. Hook, D. Mulla, V. Isler, "Sensor planning for a symbiotic UAV and UGV system for precision agriculture," IEEE Transactions on Robotics, 2016, 32(6): 1498-1511. [CrossRef] [Web of Science Times Cited 287] [SCOPUS Times Cited 364] [2] Kaur P, Kumar R, Kumar M, "A healthcare monitoring system using random forest and internet of things (IoT)". Multimedia Tools and Applications, 2019, 78: 19905-19916. [CrossRef] [Web of Science Times Cited 112] [SCOPUS Times Cited 206] [3] Ouyang, F., Cheng, H., Lan, Y, "Automatic delivery and recovery system of Wireless Sensor Networks (WSN) nodes based on UAV for agricultural applications," Computers and electronics in agriculture, 2019, 162: 31-43. [CrossRef] [Web of Science Times Cited 36] [SCOPUS Times Cited 43] [4] M. Mozaffari, W. Saad, M. Bennis, M. Debbah, "Mobile unmanned aerial vehicles (UAVs) for energy-efficient Internet of Things communications," IEEE Transactions on Wireless Communications, 2017, 16(11): 7574-7589. [CrossRef] [Web of Science Times Cited 695] [SCOPUS Times Cited 825] [5] Z. Wei, M. Zhu, N. Zhang, L. Wang, Y. Zou, Z. Meng, Z. Feng, "UAV-assisted data collection for internet of things: A survey," IEEE Internet of Things Journal, 2022, 9(17): 15460-15483. [CrossRef] [Web of Science Times Cited 60] [SCOPUS Times Cited 93] [6] Li, X., Tan, J., Liu, A., Vijayakumar, "A novel UAV-enabled data collection scheme for intelligent transportation system through UAV speed control." IEEE Transactions on Intelligent Transportation Systems 22.4 (2020): 2100-2110. [CrossRef] [Web of Science Times Cited 88] [SCOPUS Times Cited 103] [7] A. Sungheetha, R. Sharma, "Real time monitoring and fire detection using internet of things and cloud based drones," Journal of Soft Computing Paradigm (JSCP), 2020, 2(03): 168-174. [CrossRef] [8] K. Li, W. Ni, E. Tovar, M. Guizani, "Joint flight cruise control and data collection in UAV-aided internet of things: An onboard deep reinforcement learning approach," IEEE Internet of Things Journal, 2020, 8(12): 9787-9799. [CrossRef] [Web of Science Times Cited 41] [SCOPUS Times Cited 42] [9] J. Liu, X. Wang, B. Bai, H. Dai, "Age-optimal trajectory planning for UAV-assisted data collection," IEEE INFOCOM 2018-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE, 2018, pp. 553-558. [CrossRef] [SCOPUS Times Cited 179] [10] S. Aggarwal, N. Kumar, "Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges," Computer Communications, 2020, 149: 270-299. [CrossRef] [Web of Science Times Cited 365] [SCOPUS Times Cited 505] [11] G. Rigatos, P. Siano, D. Selisteanu, R. E. Precup, "Nonlinear optimal control of oxygen and carbon dioxide levels in blood," Intelligent Industrial Systems, 2017, 3: 61-75. [CrossRef] [12] H. Ucgun, I. Okten, U. Yuzgec, M. Kesler, "Test platform and graphical user interface design for vertical take-off and landing drones," Science and Technology, 2022, 25(3): 350-367. [13] R. E. Precup, S. Preitl, J. K. Tar, M. L. Tomescu, M. Takács, P. Korondi, P. Baranyi, "Fuzzy control system performance enhancement by iterative learning control," IEEE Transactions on Industrial Electronics, 2008, 55(9): 3461-3475. [CrossRef] [Web of Science Times Cited 78] [SCOPUS Times Cited 100] [14] I. A. Zamfirache, R. E. Precup, R. C. Roman, E. M. Petriu, "Neural Network-based control using Actor-Critic Reinforcement Learning and Grey Wolf Optimizer with experimental servo system validation," Expert Systems with Applications, 2023, 225: 120112. [CrossRef] [Web of Science Times Cited 40] [SCOPUS Times Cited 46] [15] Z. Yang, C. Pan, K. Wang, M. Shikh-Bahaei, "Energy efficient resource allocation in UAV-enabled mobile edge computing networks," IEEE Transactions on Wireless Communications, 2019, 18(9): 4576-4589. [CrossRef] [Web of Science Times Cited 277] [SCOPUS Times Cited 311] [16] J. Zhang, L. Zhou, F. Zhou, B. C. Seet, H. Zhang, Z. Cai, J. Wei, "Computation-efficient offloading and trajectory scheduling for multi-UAV assisted mobile edge computing," IEEE Transactions on Vehicular Technology, 2019, 69(2): 2114-2125. [CrossRef] [Web of Science Times Cited 139] [SCOPUS Times Cited 156] [17] O. Ghdiri, W. Jaafar, S. Alfattani, J. B. Abderrazak, H. Yanikomeroglu, "Offline and online UAV-enabled data collection in time-constrained IoT networks," IEEE Transactions on Green Communications and Networking, 2021, 5(4): 1918-1933. [CrossRef] [Web of Science Times Cited 26] [SCOPUS Times Cited 32] [18] M. Samir, S. Sharafeddine, C. M. Assi, T. M. Nguyen, A. Ghrayeb, "UAV trajectory planning for data collection from time-constrained IoT devices," IEEE Transactions on Wireless Communications, 2019, 19(1): 34-46. [CrossRef] [Web of Science Times Cited 235] [SCOPUS Times Cited 260] [19] O. Ghdiri, W. Jaafar, S. Alfattani, J. B. Abderrazak, H. Yanikomeroglu, "Energy-efficient multi-UAV data collection for IoT networks with time deadlines," IEEE Global Communications Conference, 2020, pp. 1-6. [CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 28] [20] S. Shen, K. Yang, K. Wang, G. Zhang, H. Mei, "Number and Operation Time Minimization for Multi-UAV-Enabled Data Collection System With Time Windows," IEEE Internet of Things Journal, 2021, 9(12): 10149-10161. [CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 17] [21] K. Liu, J. Zheng, "UAV Trajectory Optimization for Time-Constrained Data Collection in UAV-Enabled Environmental Monitoring Systems," IEEE Internet of Things Journal, 2022, 9(23): 24300-24314. [CrossRef] [Web of Science Times Cited 31] [SCOPUS Times Cited 43] [22] K. Arulkumaran, M. P. Deisenroth, M. Brundage, A. A. Bharath, "Deep reinforcement learning: A brief survey," IEEE Signal Processing Magazine, 2017, 34(6): 26-38. [CrossRef] [Web of Science Times Cited 1781] [SCOPUS Times Cited 2187] [23] J. Chen, Q. Wu, Y. Xu, N. Qi, X. Guan, Y. Zhang, Z. Xue, "Joint task assignment and spectrum allocation in heterogeneous UAV communication networks: A coalition formation game-theoretic approach," IEEE Transactions on Wireless Communications, 2020, 20(1): 440-452. [CrossRef] [Web of Science Times Cited 61] [SCOPUS Times Cited 77] [24] J. Chen, F. Ye, Y. Li, "Travelling salesman problem for UAV path planning with two parallel optimization algorithms," IEEE 2017 progress in electromagnetics research symposium-fall (PIERS-FALL), 2017, pp. 832-837. [CrossRef] [SCOPUS Times Cited 48] [25] L. P. Kaelblin, M. L. Littman, A. W. Moore, "Reinforcement learning: A survey," Journal of artificial intelligence research, 1996, 4: 237-285. [CrossRef] [Web of Science Times Cited 4074] [SCOPUS Times Cited 5599] [26] B. Kiumarsi, F. L. Lewis, H. Modares, A. Karimpour, M. B. Naghibi-Sistani, "Reinforcement Q-learning for optimal tracking control of linear discrete-time systems with unknown dynamics," Automatica, 2014, 50(4): 1167-1175. [CrossRef] [Web of Science Times Cited 362] [SCOPUS Times Cited 423] [27] V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, D. Hassabis, "Human-level control through deep reinforcement learning," nature, 2015, 518(7540): 529-533. [CrossRef] [Web of Science Times Cited 14769] [SCOPUS Times Cited 19472] [28] H. V. Hasselt, A. Guez, D. Silver, "Deep reinforcement learning with double q-learning," In Proceedings of the AAAI conference on artificial intelligence, 2016, 30(1). [CrossRef] [29] R. Y. Chen, S. Sidor, P. Abbeel, J. Schulman, "Ucb exploration via q-ensembles," arXiv preprint, 2017, 1706.01502. [CrossRef] [30] T. Schaul, J. Quan, I. Antonoglou, D. Silver, "Prioritized experience replay," arXiv preprint, 2015, 1511.05952. [CrossRef] [31] P. Kumar, T. Amgoth, C. S. R. Annavarapu, "ACO-based mobile sink path determination for wireless sensor networks under non-uniform data constraints," Applied Soft Computing, 2018, 69: 528-540. [CrossRef] [Web of Science Times Cited 104] [SCOPUS Times Cited 128] Web of Science® Citations for all references: 23,692 TCR SCOPUS® Citations for all references: 31,287 TCR Web of Science® Average Citations per reference: 740 ACR SCOPUS® Average Citations per reference: 978 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 2024-06-30 07:01 in 205 seconds. Note1: Web of Science® is a registered trademark of Clarivate Analytics. Note2: SCOPUS® is a registered trademark of Elsevier B.V. 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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.
