Autonomous drone hunter operating by deep learning and all-onboard computations in GPS-denied environments
This paper proposes a UAV platform that autonomously detects, hunts, and takes down other small UAVs in GPS-denied environments. The platform detects, tracks, and follows another drone within its sensor range using a pre-trained machine learning model. We collect and generate a 58,647-image dataset and use it to train a Tiny YOLO detection algorithm. This algorithm combined with a simple visual-servoing approach was validated on a physical platform. Our platform was able to successfully track and follow a target drone at an estimated speed of 1.5 m/s. Performance was limited by the detection algorithm's 77% accuracy in cluttered environments and the frame rate of eight frames per second along with the field of view of the camera.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2019 |
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| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:14 |
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| Enthalten in: |
PloS one - 14(2019), 11 vom: 18., Seite e0225092 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wyder, Philippe Martin [VerfasserIn] |
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| Links: |
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| Themen: |
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| Anmerkungen: |
Date Completed 23.03.2020 Date Revised 23.03.2020 published: Electronic-eCollection Citation Status MEDLINE |
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| doi: |
10.1371/journal.pone.0225092 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM303419091 |
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| 245 | 1 | 0 | |a Autonomous drone hunter operating by deep learning and all-onboard computations in GPS-denied environments |
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| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a This paper proposes a UAV platform that autonomously detects, hunts, and takes down other small UAVs in GPS-denied environments. The platform detects, tracks, and follows another drone within its sensor range using a pre-trained machine learning model. We collect and generate a 58,647-image dataset and use it to train a Tiny YOLO detection algorithm. This algorithm combined with a simple visual-servoing approach was validated on a physical platform. Our platform was able to successfully track and follow a target drone at an estimated speed of 1.5 m/s. Performance was limited by the detection algorithm's 77% accuracy in cluttered environments and the frame rate of eight frames per second along with the field of view of the camera | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, U.S. Gov't, Non-P.H.S. | |
| 700 | 1 | |a Chen, Yan-Song |e verfasserin |4 aut | |
| 700 | 1 | |a Lasrado, Adrian J |e verfasserin |4 aut | |
| 700 | 1 | |a Pelles, Rafael J |e verfasserin |4 aut | |
| 700 | 1 | |a Kwiatkowski, Robert |e verfasserin |4 aut | |
| 700 | 1 | |a Comas, Edith O A |e verfasserin |4 aut | |
| 700 | 1 | |a Kennedy, Richard |e verfasserin |4 aut | |
| 700 | 1 | |a Mangla, Arjun |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Zixi |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Xiaotian |e verfasserin |4 aut | |
| 700 | 1 | |a Xiong, Zhiyao |e verfasserin |4 aut | |
| 700 | 1 | |a Aharoni, Tomer |e verfasserin |4 aut | |
| 700 | 1 | |a Chuang, Tzu-Chan |e verfasserin |4 aut | |
| 700 | 1 | |a Lipson, Hod |e verfasserin |4 aut | |
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