DOI: https://doi.org/10.36719/2789-6919/57/231-236
Ismayil Valiyev
Azerbaijan State University of Economics
Master's student
https://orcid.org/0009-0007-4376-9804
vliyevismayil70@gmail.com
Machine Learning Approaches for Real-Time Decision
Making in Drones
Abstract
With the rapid advancement of modern aviation systems, unmanned aerial vehicles (drones) have found widespread application in both military and civilian domains. Real-time decision making is one of the most critical technical requirements directly determining drone effectiveness. Traditional command-and-control systems lack sufficient agility to respond in complex and dynamic environments. This paper comprehensively examines the application of machine learning methods for real-time decision making in drones. Within the research framework, existing deep learning, reinforcement learning, and hybrid approaches are analyzed, along with their advantages, limitations, and practical implementation prospects. Computational resource constraints, latency requirements, and environmental uncertainty are identified as key criteria for real-time systems. The paper proposes an adaptive decision index model to evaluate and select the most appropriate machine learning approach based on the operational context.
Keywords: drone, unmanned aerial vehicle, machine learning, deep learning, reinforcement learning, real-time decision making, autonomous systems, neural network