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.
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