A COMPREHENSIVE REVIEW OF ARTIFICIAL INTELLIGENCE-BASED UAV NAVIGATION METHODS

DOI 10.31673/2412-4338.2025.024074

Abstract

Vehicles (UAVs), delving into the evolution, methodologies, and emerging trends that define this dynamic field. It provides a comprehensive review of state-of-the-art techniques in UAV navigation by categorizing them into two primary paradigms: optimization-based methods and learning-based methods. The article begins with a historical overview that outlines key milestones and technological breakthroughs — from early deterministic, rulebased algorithms to advanced AI-driven systems — laying the foundation for understanding current approaches.

The article describes optimization-based methods, considering both classical and advanced techniques, including algorithms such as Dijkstra, A*, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Differential Evolution (DE), Simulated Annealing (SA), Genetic Algorithms (GA), Grey Wolf Optimization (GWO), and Pigeon-Inspired Optimization (PIO). It details their fundamentals, operating principles, and the recent modifications that researchers have employed to meet specific navigational objectives. Similarly, the review categorizes learning-based methods by examining Reinforcement Learning (RL), Deep Reinforcement Learning (DRL), Asynchronous Advantage Actor-Critic (A3C), and Deep Learning (DL) techniques, emphasizing various proposed approaches, their benefits, and the goals they aim to achieve.

As a result, this article offers a comprehensive analysis of existing navigation methods, describing their features, drawbacks, and inherent complexity. Although AI-driven navigation can be computationally expensive, the significant improvements in flexibility and overall performance enhance UAV robustness in complex dynamic environments. These findings provide insights for researchers and developers, helping to choose the most suitable approach for their work while highlighting the promise of hybrid strategies that combine the deterministic reliability of optimization techniques with the adaptability of learning-based methods.

Finally, the review identifies current research gaps — such as the need for improved big data processing, increased computing power, enhanced energy efficiency, and better fault handling — and outlines future research directions to accelerate advancements in autonomous UAV navigation. These insights provide clear guidance for future studies aimed at developing more robust, scalable, and efficient UAV navigation systems.

Keywords: navigation, artificial intelligence, unmanned aerial vehicles, UAV, path planning, autonomous navigation, trajectory planning, obstacle avoidance.