Transactions of Nanjing University of Aeronautics & Astronautics
A Collaborative Deployment Method of UAV Hangar Siting for Forest Inspection
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Abstract:
The deployment of unmanned aerial vehicle （UAV） hangars is critical to the efficiency of forest inspections， significantly influencing both infrastructure construction costs and operational expenses. Existing research on hangar selection often overlooks the complex constraints posed by forest environments， such as topographical variability， power limitations， and coverage demands. To tackle these challenges， this paper presents a multi-objective optimization approach for UAV hangar selection in forest environments， aiming to reduce construction costs while maximizing coverage under complex topographical constraints. The process begins with the preliminary selection of candidate hangars， utilizing geographic data such as the digital elevation model （DEM）， meteorological data， and power/signal coverage. A multi-criteria decision analysis （MCDA） method evaluates and scores candidates based on rigid and flexible criteria， including topographical suitability， wind speed， and power supply availability. A multi-objective optimization model is then developed to optimize the layout of hangars， incorporating critical constraints such as topographical characteristics， UAV power limits， and coverage redundancy. To solve this optimization problem， the non-dominated sorting genetic algorithm Ⅱ （NSGA-Ⅱ） is applied. Experimental results demonstrate that the proposed method outperforms traditional approaches， such as the greedy algorithm and the single-objective genetic algorithm. Specifically， the NSGA-Ⅱ method reduces the number of hangars by 8.3%， and increases the coverage by 1.6%. It also significantly accelerates the convergence， demonstrating superior performance and efficiency. This methodology provides a comprehensive solution for UAV deployment in forest inspections and can be adapted to other complex topography.
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This work was supported by the National Natural Science Foundation of China （No. 52172328）， the National Key RD Program of China （No. 2022YFB2602403）， and Postgraduate Research Practice Innovation Program of Jiangsu Province （No.KYCX24_0598）.
Clc Number:
V355
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