Evaluating AI-Driven Control Mechanisms for UAVs in Environmental Applications

Abstract

Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, have witnessed rapid growth in applications such as environmental monitoring, disaster response, surveillance, and logistics. The effectiveness and safety of UAV operations rely heavily on the implementation of advanced control strategies that ensure accurate trajectory tracking, obstacle avoidance, energy efficiency, and autonomous mission execution. This paper presents a comprehensive comparative study of artificial intelligence (AI)-based control methodologies designed to enhance the autonomy, adaptability, and decision-making capabilities of UAVs.

The study evaluates several prominent AI-driven approaches, including neural networks, reinforcement learning, fuzzy logic systems, and evolutionary algorithms, with a focus on their applicability to key UAV control tasks. Each technique is critically examined in terms of its control accuracy, computational efficiency, energy management, and suitability for swarm coordination in dynamic environments. Furthermore, the paper addresses real-world deployment challenges such as training data requirements, onboard computational limitations, and real-time responsiveness. By systematically analyzing the strengths, limitations, and practical feasibility of different AI paradigms, this research offers valuable insights for the development of intelligent, robust, and sustainable UAV control systems in environmental science applications.