Implementation of Machine Learning Techniques in Unmanned Aerial Vehicle Control and Its Various Applications

Abstract

AbstractAn unmanned aerial vehicle (UAV), sometimes known as a drone, is an aircraft or airborne system that is controlled remotely by an onboard computer or a human operator. The ground control station, aircraft components, and various types of sensors make up the UAV system. UAVs are categorized depending on their endurance, weight and altitude range. They can be used for multiple commercial and military applications. UAV intelligence and performance entirely depend on their ability to sense and comprehend new and unfamiliar environments and conditions. Numerous Machine Learning (ML) algorithms have recently been developed and implemented in the UAV system for this purpose. The integration of machine learning and unmanned aerial vehicles has resulted in outputs that are both fast and reliable. It will also lessen the number of real-time obstacles that UAVs confront while simultaneously boosting their capabilities. Additionally, it will pave the way for the application of UAVs in a number of different fields. The current chapter discusses in detail machine learning approaches and their integration with unmanned aerial vehicles. Additionally, it discusses the application of UAVs in various domains and their effectiveness.KeywordsUnmanned aerial vehicleMachine learningDroneClassification of UAVRecent trends and valuesUAV applications