Review on UAV Modeling Methods for Inspection Optimization

Low-altitude inspection technology is evolving into a key component of modern inspection systems due to its significant advantages in cost reduction and efficiency improvement. The efficient operation of unmanned aerial vehicle (UAV) inspections relies on a well-structured top-level planning system, whose core challenges can be summarized into three closely related decision-making layers: site selection, task allocation, and path planning. This paper systematically reviews the current research status and modeling methodologies of this integrated planning framework. Firstly, task characteristics are classified and analyzed from multiple dimensions, including inspection targets, operational scenarios, and task combinations. Subsequently, the modeling approaches, optimization objectives, and constraint systems of the three core layers—site selection, task allocation, and path planning—are elaborated in detail. By establishing a three-tier analytical framework, the review provides a more systematic and hierarchical analytical perspective for the field. Finally, addressing current research bottlenecks in system coordination, real-time responsiveness, and environmental adaptability, future research directions are outlined. These include three-level integrated optimization, cloud-edge-device collaborative computing, and robust planning through the integration of data-driven and physics-based models. The goal of this study is to offer a comprehensive reference for both theoretical research and engineering practice in the advancement of intelligent planning systems for UAV inspection.