Research on 3D Geometric Modeling Theory of Multi-constraint Environment for UAV Rescue after Earthquake

Xinyu Zhu; Jianyu Chu

doi:10.54691/xvaqfz14

Authors

Xinyu Zhu
Jianyu Chu

DOI:

https://doi.org/10.54691/xvaqfz14

Keywords:

Path Planning of UAV; Post-earthquake Rescue; Multi-constraint Environment; Improved Ant Colony Optimization Algorithm; 3D Geographical Modeling.

Abstract

The effectiveness of emergency response to earthquake disasters is crucial for the safety of lives and property. Unmanned Aerial Vehicles (UAVs), with their unique mobility, have emerged as a key force in reopening lifelines after earthquakes. However, the post-earthquake environment is fraught with multiple constraints, including building collapses, secondary disasters, restricted airspace, and urgent material demands, posing significant challenges to UAV path planning. To address these challenges, this research focuses on the core issue of "UAV path planning in multi-constraint environments after earthquakes." It systematically constructs a multi-level assessment index system encompassing disaster severity, demand urgency, environmental feasibility, and rescue support level, and establishes a high-fidelity 3D geographic environment model. Furthermore, an Improved Ant Colony Optimization (IACO) algorithm is proposed. By integrating a composite heuristic function that considers task priority and environmental risks, along with a dynamic pheromone update strategy, the algorithm effectively balances path economy, safety, and mission timeliness. This study aims to form a complete technical closed-loop from "environmental perception" to "intelligent decision-making," providing a solid theoretical and technical foundation for precise UAV rescue operations in complex disaster scenarios.

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