摘要
灾害场景下依托无人机配送资源应用前景广阔,但应急场景环境复杂多变,各类突发事件在时空上的不确定性会导致目标点对资源需求评估的不准确,进而影响到资源配送中无人机任务分配方案.针对此问题,在无人机任务分配模型中引入两阶段鲁棒优化方法.模型通过将无人机分配和任务分配相结合,充分利用无人机集群资源,实现需求变化最大化时的任务分配成本最低.本文对受伤人数等级与资源需求变化关系建模,将资源需求划分为3个等级,实现了任务分配总成本变化的精确化表达,并采用列和约束生成(Column-and-Constraint Generation,C&CG)算法实现了资源需求不确定条件下的无人机任务分配.最后设计了3种类型的实验,仿真结果验证了算法的有效性和优越性,相比确定性模型,该算法在应对需求变化时展现出更好的鲁棒性.
In disaster scenarios,the application of UAV(Unmanned Aerial Vehicle)for resource delivery holds considerable promise.However,the complexity and volatility of emergency environments,along with the spatial and temporal uncertainties associated with various unexpected events,can lead to inaccuracies in assessing resource demands at target points,which in turn may affect the UAV task allocation strategies in resource distribution.To address this issue,a twostage robust optimization approach is introduced into the UAV task assignmet model.By integrating UAV assignment with task allocation,the model leverages the resources of the UAV fleet to minimize task assignment costs under maximum demand variability.This paper models the relationship between injury severity levels and resource demand variations,categorizing resource demand into three levels to achieve an accurate representation of total task allocation cost variations.The C&CG(Column-and-Constraint Generation)algorithm is used to address UAV task assignment under uncertain resource demand conditions.Finally,three types of experiments were designed and the simulation results validated the effectiveness and superiority of the algorithm.Compared to the deterministic model,this algorithm showed greater robustness in handling demand variation.
作者
王巍
解慧
魏忠诚
赵继军
彭力
WANG Wei;XIE Hui;WEI Zhong-cheng;ZHAO Ji-jun;PENG Li(School of Information&Electrical Engineering,Hebei University of Engineering,Handan,Hebei 056038,China;Hebei Key Laboratory of Security&Protection Information Sensing and Processing,Handan,Hebei 056038,China;School of Electrical Engineering and Information,Southwest Petroleum University,Chengdu,Sichuan 610500,China;School of Internet of Things Engineering,Jiangnan University,Wuxi,Jiangsu 214122,China)
出处
《电子学报》
EI
CAS
CSCD
北大核心
2024年第10期3552-3561,共10页
Acta Electronica Sinica
基金
国家重点研发计划(No.2018YFF0301004)
国家自然科学基金(No.61802107)
河北省高等学校科学技术研究项目(No.ZD2020171)
江苏省博士后科研资助计划项目(No.1601085C)。