The growing interest in the utilization of Unmanned Aerial Vehicles (UAVs) demands minimizing the costs of robot maintenance, where one of the main aspects relates to energy consumption. This manuscript presents a novel approach to create an energy consumption model for UAVs. The authors prove, based on experimentally collected data using a drone carrying various payloads, that Machine Learning (ML) algorithms allow to sufficiently accurately estimate a power signal. As opposed to the classical approach with mathematical modeling, the presented method does not require any knowledge about the drone’s construction, thus making it a universal tool. Calculated metrics show the Decision Tree is the most suitable algorithm among eight different ML methods due to its high energy prediction accuracy of at least 97.5% and a short learning time which was equal to 2 ms for the largest dataset.

A detailed literature analysis depicts that artificial neural networks are rarely used for the power consumption estimation in the mobile robotics field. Instead, researchers prefer to develop analytical models of investigated robots. This manuscript presents a comparison of mathematical models and non-complex artificial neural networks in energy prediction tasks for differential and skid-steer drive robots which move over various types of surfaces. The results show that both methods could be used interchangeably but AI methods are more universal, do not depend on the kinematic structure of a robot and are tolerant for designers not having a complex knowledge about the system.

The growing significance of mobile robots in a full spectrum of areas of life creates new challenges and opportunities in robotics. One critical aspect to consider is energy utilization, as accurate prediction plays a vital role in a robot’s reliability and safety. Furthermore, precise prediction offers economic advantages, particularly for robotic fleets, where energy management systems can optimize maintenance costs and operational efficiency. The following review describes the state of the art of energy usage prediction for different types of mobile robots, highlights current trends, and analyses algorithms’ complexity (in implementation and execution), accuracy, and universality.