考虑多约束因素的物流无人机网络高精度风险分析模型OACSTPCD
A High-Precision Risk Analysis Model of Logistics UAV Network with Multiple Constraining Factors
物流无人机为全球快递物流业的扩张带来了新的机遇,有效克服了地面运输方式的不足.当前物流无人机仍处于起步阶段,因此有必要分析其运行过程中的碰撞风险.本文采用冲突区碰撞建模理论,根据物流无人机的特点和局限性,研究了其在特定空域飞行内的安全隐患.首先,为了衡量可靠性和故障率等多种因素对物流无人机在特定空域安全运行的影响,建立了物流无人机与其他无人机在特定空域的碰撞风险分析模型.然后,通过分析影响物流无人机安全运行的因素,包括空域条件、人机系统、环境条件和管理条件,建立了在特定空域运行的物流无人机与民用飞机碰撞风险分析模型.为了验证所提出模型的准确性,本研究对这两种情况下的模型进行了求解,并与国际民航组织制定的安全性标准进行了比较.
Logistics unmanned aerial vehicles(UAVs)have brought new opportunities for the expansion of the global express logistics industry,especially to effectively overcome the shortcomings of ground transportation.However,since logistics UAVs are still in their infancy,it is necessary to analyze the collision risk during their operation.Using the theory of collision modeling in conflict zones,this study examines the potential safety hazards of logistics UAVs flying in specific airspace according to their characteristics and limitations.First,to measure the impact of various factors such as reliability and failure rates on the safe operation of logistics UAVs in certain airspace,a collision risk analysis model between logistics UAVs and other drones in a specific airspace is established.Second,by analyzing the factors that affect the safe operation of logistics UAVs,including airspace conditions,human-machine systems,environmental conditions,and management conditions,a collision risk analysis model between logistics UAVs and civil aircraft operating in particular airspace is established.To verify the accuracy of the proposed models,the models in both cases are solved and compared with the safety target criteria established by the International Civil Aviation Organization(ICAO).
闫永刚;李新飞;沈志远;魏文斌
南京航空航天大学民航学院,南京 211106,中国||中国民用航空局空中交通管理局,北京 100022,中国中国南方航空有限公司北京分公司,北京 102602,中国南京航空航天大学民航学院,南京 211106,中国圣何塞州立大学工程学院,圣何塞95192-0061,美国
物流无人机冲突模型冲突风险低空经济安全运行
logistics unmanned aerial vehicle(UAV)collision modelcollision risklow-altitude economysafe operation
《南京航空航天大学学报(英文版)》 2024 (002)
218-232 / 15
This work was supported by the Na-tional Natural Science Foundation of China(No.U2233208).
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