Source of Publication
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.
unmanned aerial vehicle (UAV), autonomous landing, deep-neural network, reinforcement learning, multi-level quantization, Q-learning
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This work is licensed under a Creative Commons Attribution 4.0 International License.
Abo Mosali, Najmaddin; Shamsudin, Syariful Syafiq; Mostafa, Salama A.; Alfandi, Omar; Omar, Rosli; Al-Fadhali, Najib; Mohammed, Mazin Abed; Malik, R. Q.; Jaber, Mustafa Musa; and Saif, Abdu, "An Adaptive Multi-Level Quantization-Based Reinforcement Learning Model for Enhancing UAV Landing on Moving Targets" (2022). All Works. 5274.
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Gold: This publication is openly available in an open access journal/series