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Collaborative Vehicle-Mounted Multi-UAV Routing and Scheduling Optimization for Remote Sensing Observations

文献类型：期刊论文

作者Du, Bing1,2,3; Tang, Anqi1,2,3; Ye, Huping1,2,3; Yue, Huanyin1,2,3; Xu, Chenchen1,2,3; Hao, Lina1,2,3; He, Hongbo1,2,3; Liao, Xiaohan1,2,3
刊名DRONES
出版日期2025-11-11
卷号9期号:11页码:783
关键词unmanned aerial vehicle scheduling optimization genetic algorithm coverage path planning remote sensing emergency management
DOI10.3390/drones9110783
产权排序1
文献子类Article
英文摘要Highlights What are the main findings? A multi-UAV routing and scheduling framework for remote sensing observation tasks is proposed, and comparative experiments with classical algorithms demonstrate the superiority of the proposed method. A Multi-Region Edge Recombination Crossover operator and an Adaptive Hybrid Mutation mechanism are designed to enhance the genetic algorithm's performance in multi-UAV task scheduling and routing optimization. What are the implications of the main findings? The routing and scheduling schemes of vehicle-mounted multi-UAV systems are optimized, significantly reducing the coverage cost of remote sensing observations. The proposed framework provides practical scheduling guidance for large-scale remote sensing applications using vehicle-mounted multi-UAV systems, effectively minimizing task redundancy.Highlights What are the main findings? A multi-UAV routing and scheduling framework for remote sensing observation tasks is proposed, and comparative experiments with classical algorithms demonstrate the superiority of the proposed method. A Multi-Region Edge Recombination Crossover operator and an Adaptive Hybrid Mutation mechanism are designed to enhance the genetic algorithm's performance in multi-UAV task scheduling and routing optimization. What are the implications of the main findings? The routing and scheduling schemes of vehicle-mounted multi-UAV systems are optimized, significantly reducing the coverage cost of remote sensing observations. The proposed framework provides practical scheduling guidance for large-scale remote sensing applications using vehicle-mounted multi-UAV systems, effectively minimizing task redundancy.Abstract Vehicle-mounted multi-UAV (VM-UAV) systems offer enhanced flexibility and rapid deployment for large-scale remote sensing tasks such as disaster response and land surveys. However, maximizing their operational efficiency remains challenging, as it requires the simultaneous resolution of task scheduling and coverage path planning-an NP-hard problem. This study presents a novel multi-objective genetic algorithm (GA) framework that jointly optimizes routing and scheduling for cost-constrained, load-balanced multi-UAV remote sensing missions. To improve convergence speed and solution quality, we introduce two innovative operators: a Multi-Region Edge Recombination Crossover (MRECX) to preserve superior path segments from parents and an Adaptive Hybrid Mutation (AHM) mechanism that dynamically adjusts mutation strategies to balance exploration and exploitation. The algorithm minimizes total flight distance while equalizing workload distribution among UAVs. Extensive simulations and experiments demonstrate that the proposed GA significantly outperforms conventional GA, particle swarm optimization (PSO), ant colony optimization (ACO), and clustering-based planning methods in both solution quality and robustness. The practical applicability of our framework is further validated through two real-world case studies. The results confirm that the proposed approach delivers an effective and scalable solution for vehicle-mounted multi-UAV scheduling and path planning, enhancing operational efficiency in time-critical remote sensing applications.
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WOS关键词PATH ; ALGORITHM
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:001624664400001
出版者MDPI
源URL[http://ir.igsnrr.ac.cn/handle/311030/219495]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Liao, Xiaohan
作者单位1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China;
2.Civil Aviat Adm China, Key Lab Low Altitude Geog Informat & Air Route, Beijing 100101, Peoples R China;
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Du, Bing,Tang, Anqi,Ye, Huping,et al. Collaborative Vehicle-Mounted Multi-UAV Routing and Scheduling Optimization for Remote Sensing Observations[J]. DRONES,2025,9(11):783.
APA Du, Bing.,Tang, Anqi.,Ye, Huping.,Yue, Huanyin.,Xu, Chenchen.,...&Liao, Xiaohan.(2025).Collaborative Vehicle-Mounted Multi-UAV Routing and Scheduling Optimization for Remote Sensing Observations.DRONES,9(11),783.
MLA Du, Bing,et al."Collaborative Vehicle-Mounted Multi-UAV Routing and Scheduling Optimization for Remote Sensing Observations".DRONES 9.11(2025):783.

入库方式： OAI收割

来源：地理科学与资源研究所

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