An adaptive multi-level quantization-based reinforcement learning model for enhancing UAV landing on moving targets

Abo Mosali, Najmaddin and Shamsudin, Syariful Syafiq and Mostafa, Salama A. and Alfandi, Omar and Omar, Rosli and Al-Fadhali, Najib and Mohammed, Mazin Abed and Malik, R. Q. and Jaber, Mustafa Musa and Saif, Abdu (2022) An adaptive multi-level quantization-based reinforcement learning model for enhancing UAV landing on moving targets. Sustainability, 14 (14). ISSN 2071-1050, DOI

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The autonomous landing of an unmanned aerial vehicle (UAV) on a moving platform is an essential functionality in various UAV-based applications. It can be added to a teleoperation UAV system or part of an autonomous UAV control system. Various robust and predictive control systems based on the traditional control theory are used for operating a UAV. Recently, some attempts were made to land a UAV on a moving target using reinforcement learning (RL). Vision is used as a typical way of sensing and detecting the moving target. Mainly, the related works have deployed a deep-neural network (DNN) for RL, which takes the image as input and provides the optimal navigation action as output. However, the delay of the multi-layer topology of the deep neural network affects the real-time aspect of such control. This paper proposes an adaptive multi-level quantization-based reinforcement learning (AMLQ) model. The AMLQ model quantizes the continuous actions and states to directly incorporate simple Q-learning to resolve the delay issue. This solution makes the training faster and enables simple knowledge representation without needing the DNN. For evaluation, the AMLQ model was compared with state-of-art approaches and was found to be superior in terms of root mean square error (RMSE), which was 8.7052 compared with the proportional-integral-derivative (PID) controller, which achieved an RMSE of 10.0592.

Item Type: Article
Funders: Zayed University cluster award (Grant No: R19046), Ministry of Higher Education (MoHE) through the Fundamental Research Grant Scheme (Grant No: FRGS/1/2021/ICT01/UTHM/02/1 & K389), Universiti Tun Hussein Onn Malaysia, UTHM Publisher's Office via Publication Fund (Grant No: E15216)
Uncontrolled Keywords: Unmanned aerial vehicle (UAV); Autonomous landing; Deep-neural network; Reinforcement learning; Multi-level quantization; Q-learning
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering > Department of Electrical Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 27 Oct 2023 09:12
Last Modified: 27 Oct 2023 09:12

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