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UNMANNED AERIAL VEHICLES PATH-PLANNING STRATEGIES: FROM CLASSICAL METHODS TO ARTIFICIAL INTELLIGENCE-BASED APPROACHES
Abstract
Unmanned Aerial Vehicles (UAVs) have become essential platforms in domains such as logistics, environmental monitoring, surveillance, agriculture, and disaster management. The growing complexity of missions and the increasing demand for autonomy highlight the critical role of trajectory optimization, which ensures safe, efficient, and adaptive navigation in dynamic environments. Path-planning algorithms form the core of this capability, enabling UAVs to avoid obstacles, minimize energy consumption, and accomplish mission objectives under diverse operational constraints. A wide range of algorithms has been proposed for UAV path-planning, reflecting different trade-offs between efficiency, adaptability, and robustness. Classical graph-based methods, sampling-based approaches, and artificial potential fields provide established solutions, while bio-inspired metaheuristics offer improved optimization capabilities. More recently, machine learning and deep learning strategies have further expanded the toolkit, enabling enhanced adaptability in dynamic environments. Each class of algorithms exhibits distinct advantages and limitations in terms of computational cost, scalability, real-time performance, and trajectory optimality. This review systematically analyses these algorithmic approaches, emphasizing their principles, performance, and applicability, while highlighting unresolved challenges and outlining pathways toward fully autonomous aerial navigation.
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References14
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