SavedAdd your journal

Journals / European Journal of Technique (EJT) / 2023 / Cilt: 13 Sayı: 1

Q-Learning Based Obstacle Avoidance Data Harvesting Model Using UAV and UGV

Erdal AKİN
Yakup ŞAHİN
Pages
54–60
Publication Date
2023-06-30
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This research aims to improve the overall efficiency and effectiveness of data collection in IoT environments and pave the way for sustainable IoT solutions.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The Internet of Things (IoT) has revolutionized our lives by providing convenience in various aspects of our lives.
📊
Key Findings & Data:This is because timely data collection enables more accurate evaluations and insights.
💡
Conclusion & Impact:This research aims to improve the overall efficiency and effectiveness of data collection in IoT environments and pave the way for sustainable IoT solutions.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Q-Learning · UAV · UGV

Abstract

The Internet of Things (IoT) has revolutionized our lives by providing convenience in various aspects of our lives. However, for the IoT environment to function optimally, it is crucial to regularly collect data from IoT devices. This is because timely data collection enables more accurate evaluations and insights. Additionally, energy conservation is another crucial aspect to consider when collecting data, as it can have a significant impact on the sustainability of the IoT ecosystem. To achieve this, Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) are increasingly being used to collect data. In this study, we delve into the problem of how UAVs and UGVs can effectively and efficiently collect data from IoT devices in an environment with obstacles. To address this challenge, we propose a Q-learning-based Obstacle Avoidance Data Harvesting (QOA-DH) method, which utilizes the principles of reinforcement learning to make decisions on data collection. Additionally, we conduct a comparison of the performance of UAVs and UGVs, considering the different restrictions and assumptions that are unique to each type of vehicle. This research aims to improve the overall efficiency and effectiveness of data collection in IoT environments and pave the way for sustainable IoT solutions.

🔖

Cite This Article

AKİN, E., ŞAHİN, Y. (2023). Q-Learning Based Obstacle Avoidance Data Harvesting Model Using UAV and UGV. European Journal of Technique (EJT), Cilt: 13 Sayı: 1, 54–60. https://ajindex.com/articles/955335

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (12)

  1. Z. Qin, X. Zhang, X. Zhang, B. Lu, Z. Liu, and L. Guo, “The uav trajectory optimization for data collection from time-constrained iot devices: A hierarchical deep q-network approach,” Applied Sciences, vol. 12, no. 5, pp. 2546, 2022
  2. Bithas, P.S., Michailidis, E.T., Nomikos, N., Vouyioukas, D. and Kanatas, A.G., 2019. A survey on machine-learning techniques for UAV-based communications. Sensors, vol. 19, no. 23, pp.5170
  3. H. Bayerlein, M. Theile, M. Caccamo, and D. Gesbert, “Multi-UAV path planning for wireless data harvesting with deep reinforcement learning,” IEEE Open Journal of the Communications Society, vol. 2, pp. 1171– 1187, 2021
  4. Y. Yao, Z. Zhu, S. Huang, X. Yue, C. Pan, and X. Li, “Energy efficiency characterization in heterogeneous iot system with uav swarms based on wireless power transfer,“ IEEE Access, vol. 8, pp. 967–979, 2019
  5. Z. Wang and J. Cai, “Probabilistic roadmap method for path-planning in radioactive environment of -nuclear facilities,” Progress in Nuclear Energy, vol. 109, pp.113–120, 2018
  6. A. Upadhyay, K. R. Shrimali, and A. Shukla, “Uav-robot relationship for coordination of robots on a collision free path,” Procedia Computer Science, vol. 133, pp. 424–431, 2018
  7. F. Yan, Y.-S. Liu, and J.-Z. Xiao, “Path planning in complex 3d environments using a probabilistic roadmap method,” International Journal of Automation and computing, vol. 10, no. 6, pp. 525–533, 2016
  8. S. Jain, R. C. Shah, W. Brunette, G. Borriello, and S. Roy, “Exploiting mobility for energy efficient data collection in wireless sensor networks,” Mobile networks and Applications, vol. 11, no. 3, pp. 327–339, 2006
  9. C.S. Choi and F. Baccelli, “Spatial and temporal analysis of direct communications from static devices to mobile vehicles,” IEEE Transactions on Wireless Communications, vol. 18, no. 11, pp. 5128–5140, 2019
  10. R. Marini, L. Spampinato, S. Mignardi, R. Verdone, and C. Buratti, “Reinforcement learning-based trajectory planning for uav-aided vehicular communications,” 2022 30th European Signal Processing Conference (EUSIPCO), IEEE, pp. 967–971, 2022
  11. A. Kaplan, N. Kingry, P. Uhing, and R. Dai, “Time-optimal path planning with power schedules for a solar-powered ground robot,” IEEE Transactions on Automation Science and Engineering, vol. 14, no. 2, pp. 1235–1244, 2006
  12. M. R. Jabbarpour, H. Zarrabi, J. J. Jung, and P. Kim, “A green ant-based method for path planning of unmanned ground vehicles,” IEEE access, vol. 5, pp. 1