AERO2022-flying danger reduction for quadcopters by using machine learning to estimate current, voltage, and flight area
Choosing the appropriate battery capacity for unmanned aerial vehicle (UAV) missions is critical, as draining the battery during flight nearly always results in vehicle damage and a significant risk of human harm or property damage. Predicting energy usage on a difficult trip is critical since the f...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772671122000560 |
| _version_ | 1828130688114622464 |
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| author | Seyed Matin Malakouti Amir Rikhtehgar Ghiasi Amir Aminzadeh Ghavifekr |
| author_facet | Seyed Matin Malakouti Amir Rikhtehgar Ghiasi Amir Aminzadeh Ghavifekr |
| author_sort | Seyed Matin Malakouti |
| collection | DOAJ |
| description | Choosing the appropriate battery capacity for unmanned aerial vehicle (UAV) missions is critical, as draining the battery during flight nearly always results in vehicle damage and a significant risk of human harm or property damage. Predicting energy usage on a difficult trip is critical since the flying location, weather conditions, and other factors all impact power use. We develop a drone model that employs machine learning techniques to forecast battery and current consumption, as well as the quadcopter flying area, extremely precisely and quickly. As a result, the flight danger is lowered, and we will have a safe flight. |
| first_indexed | 2024-04-11T16:36:21Z |
| format | Article |
| id | doaj.art-c896a97a478340da940e09bd2d04d07a |
| institution | Directory Open Access Journal |
| issn | 2772-6711 |
| language | English |
| last_indexed | 2024-04-11T16:36:21Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj.art-c896a97a478340da940e09bd2d04d07a2022-12-22T04:13:50ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112022-01-012100084AERO2022-flying danger reduction for quadcopters by using machine learning to estimate current, voltage, and flight areaSeyed Matin Malakouti0Amir Rikhtehgar Ghiasi1Amir Aminzadeh Ghavifekr2Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, IranCorresponding author.; Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, IranFaculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, IranChoosing the appropriate battery capacity for unmanned aerial vehicle (UAV) missions is critical, as draining the battery during flight nearly always results in vehicle damage and a significant risk of human harm or property damage. Predicting energy usage on a difficult trip is critical since the flying location, weather conditions, and other factors all impact power use. We develop a drone model that employs machine learning techniques to forecast battery and current consumption, as well as the quadcopter flying area, extremely precisely and quickly. As a result, the flight danger is lowered, and we will have a safe flight.http://www.sciencedirect.com/science/article/pii/S2772671122000560Machine learningBattery capacityUncrewed aerial vehicleHigh riskSafe flight |
| spellingShingle | Seyed Matin Malakouti Amir Rikhtehgar Ghiasi Amir Aminzadeh Ghavifekr AERO2022-flying danger reduction for quadcopters by using machine learning to estimate current, voltage, and flight area e-Prime: Advances in Electrical Engineering, Electronics and Energy Machine learning Battery capacity Uncrewed aerial vehicle High risk Safe flight |
| title | AERO2022-flying danger reduction for quadcopters by using machine learning to estimate current, voltage, and flight area |
| title_full | AERO2022-flying danger reduction for quadcopters by using machine learning to estimate current, voltage, and flight area |
| title_fullStr | AERO2022-flying danger reduction for quadcopters by using machine learning to estimate current, voltage, and flight area |
| title_full_unstemmed | AERO2022-flying danger reduction for quadcopters by using machine learning to estimate current, voltage, and flight area |
| title_short | AERO2022-flying danger reduction for quadcopters by using machine learning to estimate current, voltage, and flight area |
| title_sort | aero2022 flying danger reduction for quadcopters by using machine learning to estimate current voltage and flight area |
| topic | Machine learning Battery capacity Uncrewed aerial vehicle High risk Safe flight |
| url | http://www.sciencedirect.com/science/article/pii/S2772671122000560 |
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