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This report adopts a two-piece MINCO parameterization, trading time for smoothness without altering the trajectory's spatial profile, and replaces score regression with a ranking loss, preventing small score errors from reordering the candidate set.
Abstract. In recent years, the increasing frequency of natural disasters in remote and rugged areas has underscored the importance of unmanned aerial vehicles (UAVs) for rapid emergency response. This paper presents a novel approach for optimizing the placement of UAV docking stations in mountainous terrain for emergency operations. We develop a comprehensive, 3D Geodata framework that integrates 3D Digital Elevation Models (3D DEM), building infrastructure, and road network data to create a realistic three-dimensional optimization environment. The proposed system employs an Enhanced Adaptive Particle Swarm Optimization (EAPSO) algorithm with adaptive parameters, diversity maintenance mechanisms, and intelligent convergence detection to effectively handle the complex constraints of mountainous environments. Experimental results demonstrate that our 3D-aware EAPSO approach achieves superior performance in balancing coverage efficiency, energy consumption, and network connectivity compared to conventional optimization methods. The proposed system provides a scientific foundation for improving emergency response capabilities in challenging geographical environments.
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