Qazzaz, M.M.H., Zaidi, S.A.R. orcid.org/0000-0003-1969-3727, McLernon, D.C. et al. (2 more authors) (2024) Optimizing Search and Rescue UAV Connectivity in Challenging Terrain Through Multi Q-Learning. In: 2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM). 2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM), 23-25 Jul 2024, Leeds, UK. IEEE ISBN 979-8-3503-7787-3
Abstract
Using Unmanned Aerial Vehicles (UAVs) in Search and rescue operations (SAR) to navigate challenging terrain while maintaining reliable communication with the cellular network is a promising approach. This paper suggests a novel technique employing a reinforcement learning multi Q-learning algorithm to optimize UAV connectivity in such scenarios. We introduce a Strategic Planning Agent for efficient path planning and collision awareness and a Real-time Adaptive Agent to maintain optimal connection with the cellular base station. The agents trained in a simulated environment using multi Q-learning, encouraging them to learn from experience and adjust their decision-making to diverse terrain complexities and communication scenarios. Evaluation results reveal the significance of the approach, highlighting successful navigation in environments with varying obstacle densities and the ability to perform optimal connectivity using different frequency bands. This work paves the way for enhanced UAV autonomy and enhanced communication reliability in search and rescue operations.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Search and Rescue, Cellular connected UAVs, SAR, path planning, UAVs, Reinforcement Learning, Q learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 12 Mar 2025 11:21 |
Last Modified: | 12 Mar 2025 11:21 |
Published Version: | https://ieeexplore.ieee.org/document/10656603 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/wincom62286.2024.10656603 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:224312 |