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Obstacle detection for Unmanned Aerial Vehicle (UAV)

This study aims to develop an obstacle detection system for unmanned aerial vehicles utilising the ORB feature extraction. In the past, small unmanned aerial vehicles (UAV) were typically equipped with vision-based or range-based sensors. Each sensor in the sensor-based technique possesses diffe...

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Main Authors: Abdul Khaliq, Nor Aizatul Nabila, Ramli, Muhammad Faiz
Format: Article
Language:English
Published: Penerbit UTHM 2022
Subjects:
T Technology (General)
Online Access:http://eprints.uthm.edu.my/8114/1/J14841_aa59537113306d1920cdbeb2e18502c2.pdf
http://eprints.uthm.edu.my/8114/
https://doi.org/10.30880/paat.2022.02.01.007
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spelling my.uthm.eprints.81142022-12-18T02:59:25Z http://eprints.uthm.edu.my/8114/ Obstacle detection for Unmanned Aerial Vehicle (UAV) Abdul Khaliq, Nor Aizatul Nabila Ramli, Muhammad Faiz T Technology (General) This study aims to develop an obstacle detection system for unmanned aerial vehicles utilising the ORB feature extraction. In the past, small unmanned aerial vehicles (UAV) were typically equipped with vision-based or range-based sensors. Each sensor in the sensor-based technique possesses different advantages and disadvantages. As a result, the small unmanned aerial vehicle is unable to determine the obstacle's distance or bearing precisely. Due to physical size restrictions and payload capacity, the lightweight Pi Camera and TF Luna LiDAR sensor were selected as the most suitable sensors for integration. In algorithm development and filtration is used to improve the accuracy of the feature matching process, which is required for classifying the obstacle region and free region of any texture obstacle. The experiment was under the environment of OpenCV and Spyder. In real-time experiment, the success rate for good texture (40%), poor texture (55%) and texture-less (45%) The findings indicate that the recommended method works well for detecting textures-less obstacle even though the success rate is only 40% because out of 10 test only one test is fail on detecting free region. The sensor calibration and constructing convex hull for obstacle detection is recommended in future works to improve the efficiency of the obstacle detection system and classified the free region and obstacle region to create safe avoidance path. Penerbit UTHM 2022 Article PeerReviewed text en http://eprints.uthm.edu.my/8114/1/J14841_aa59537113306d1920cdbeb2e18502c2.pdf Abdul Khaliq, Nor Aizatul Nabila and Ramli, Muhammad Faiz (2022) Obstacle detection for Unmanned Aerial Vehicle (UAV). PROGRESS IN AEROSPACE AND AVIATION TECHNOLOGY, 2 (1). pp. 53-64. https://doi.org/10.30880/paat.2022.02.01.007
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Abdul Khaliq, Nor Aizatul Nabila
Ramli, Muhammad Faiz
Obstacle detection for Unmanned Aerial Vehicle (UAV)
description This study aims to develop an obstacle detection system for unmanned aerial vehicles utilising the ORB feature extraction. In the past, small unmanned aerial vehicles (UAV) were typically equipped with vision-based or range-based sensors. Each sensor in the sensor-based technique possesses different advantages and disadvantages. As a result, the small unmanned aerial vehicle is unable to determine the obstacle's distance or bearing precisely. Due to physical size restrictions and payload capacity, the lightweight Pi Camera and TF Luna LiDAR sensor were selected as the most suitable sensors for integration. In algorithm development and filtration is used to improve the accuracy of the feature matching process, which is required for classifying the obstacle region and free region of any texture obstacle. The experiment was under the environment of OpenCV and Spyder. In real-time experiment, the success rate for good texture (40%), poor texture (55%) and texture-less (45%) The findings indicate that the recommended method works well for detecting textures-less obstacle even though the success rate is only 40% because out of 10 test only one test is fail on detecting free region. The sensor calibration and constructing convex hull for obstacle detection is recommended in future works to improve the efficiency of the obstacle detection system and classified the free region and obstacle region to create safe avoidance path.
format Article
author Abdul Khaliq, Nor Aizatul Nabila
Ramli, Muhammad Faiz
author_facet Abdul Khaliq, Nor Aizatul Nabila
Ramli, Muhammad Faiz
author_sort Abdul Khaliq, Nor Aizatul Nabila
title Obstacle detection for Unmanned Aerial Vehicle (UAV)
title_short Obstacle detection for Unmanned Aerial Vehicle (UAV)
title_full Obstacle detection for Unmanned Aerial Vehicle (UAV)
title_fullStr Obstacle detection for Unmanned Aerial Vehicle (UAV)
title_full_unstemmed Obstacle detection for Unmanned Aerial Vehicle (UAV)
title_sort obstacle detection for unmanned aerial vehicle (uav)
publisher Penerbit UTHM
publishDate 2022
url http://eprints.uthm.edu.my/8114/1/J14841_aa59537113306d1920cdbeb2e18502c2.pdf
http://eprints.uthm.edu.my/8114/
https://doi.org/10.30880/paat.2022.02.01.007
_version_ 1753790425312788480
score 13.211869

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