| Authors | Seyed Matin Malakouti, Amir Rikhtehgar Ghiasi, Amir Aminzadeh Ghavifekr |
|---|---|
| Journal | e-Prime-Advances in Electrical Engineering, Electronics and Energy |
| Paper Type | Full Paper |
| Published At | 2022 |
| Journal Grade | Scientific - research |
| Journal Type | Typographic |
| Journal Country | Netherlands |
Abstract
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.