Modeling Energy Consumption of Small Drones for Swarm Missions

Abstract

Drones, particularly when deployed in swarms, hold immense potential for various applications, such as aerial imaging, delivery services, disaster response, and advanced surveillance. Their effective and efficient use, however, hinges on the accurate estimation of energy consumption. This study focuses on determining energy consumption patterns for small drones weighing less than 2 kg and with diameters under 1 m. Through an extensive series of test flights, empirical energy consumption data was collected and used to develop four distinct energy models: a theoretical model based on fundamental flight forces, a simple linear regression model, a cubic polynomial regression model, and a machine learning-based model using the XGBoost Regressor algorithm. A cost function for swarm topology control was also derived for assessing energy consumption during various activities, like connectivity restoration and formation change, facilitating more energy-efficient decision-making in swarm operations. Our findings highlighted an energy-efficient “valley” within the airspeed range, indicating that flying at speeds outside this range results in higher energy consumption. The machine learning model demonstrated superior accuracy within its training range, achieving an R <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^2$</tex-math> </inline-formula> of 0.9999, whereas the polynomial regression model was deemed best for extrapolation purposes, delivering an R <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^2$</tex-math> </inline-formula> of 0.966. Simple linear regression and theoretical models, although less accurate, can offer quick energy demand calculations and insights into the effects of hardware modifications, respectively.