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

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 tradi...

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Main Authors: 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, Saif, Abdu
Format: Article
Published: MDPI 2022
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TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.um.edu.my/41645/
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spelling my.um.eprints.416452023-10-27T09:12:59Z http://eprints.um.edu.my/41645/ An adaptive multi-level quantization-based reinforcement learning model for enhancing UAV landing on moving targets 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 Saif, Abdu TK Electrical engineering. Electronics Nuclear engineering 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. MDPI 2022-07 Article PeerReviewed 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 https://doi.org/10.3390/su14148825 <https://doi.org/10.3390/su14148825>. 10.3390/su14148825
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
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
Saif, Abdu
An adaptive multi-level quantization-based reinforcement learning model for enhancing UAV landing on moving targets
description 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.
format Article
author 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
Saif, Abdu
author_facet 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
Saif, Abdu
author_sort Abo Mosali, Najmaddin
title An adaptive multi-level quantization-based reinforcement learning model for enhancing UAV landing on moving targets
title_short An adaptive multi-level quantization-based reinforcement learning model for enhancing UAV landing on moving targets
title_full An adaptive multi-level quantization-based reinforcement learning model for enhancing UAV landing on moving targets
title_fullStr An adaptive multi-level quantization-based reinforcement learning model for enhancing UAV landing on moving targets
title_full_unstemmed An adaptive multi-level quantization-based reinforcement learning model for enhancing UAV landing on moving targets
title_sort adaptive multi-level quantization-based reinforcement learning model for enhancing uav landing on moving targets
publisher MDPI
publishDate 2022
url http://eprints.um.edu.my/41645/
_version_ 1781704702350589952
score 13.211869

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