Abstract
In drone missions, accurate estimation of remaining flight time is crucial for enhancing mission duration and reducing the risk of losing drones due to power outages. This paper addresses the challenge of predicting future power consumption for a quadcopter UAV, focusing on the energy demands of planned future maneuvers. Existing physics-based energy consumption models assume the drone hovers or moves with constant speed. While orientation and velocity measurements are typically used, acceleration measurements are often ignored, despite being identified as important for power usage prediction in recent machine learning literature. This paper proposes a physics-based, lightweight energy consumption model based on measurements of the quadcopter's motion, including acceleration. Our model also includes a synthetic wind estimation scheme, enhancing prediction accuracy in outdoor and windy conditions. The model's predictions for power consumption are compared to real-life flight data, demonstrating the effectiveness of the presented approach despite its relative simplicity.