基于故障树和快照技术的无人机特情训练方法

林清; 蔡志浩; 王英勋; 陈立芳

基于故障树和快照技术的无人机特情训练方法

北京航空航天大学飞行器控制一体化技术重点实验室, 北京 100191

详细信息

中图分类号: V249
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- 被引次数: 0
出版历程
- 收稿日期: 2012-12-18
- 网络出版日期: 2013-11-30

Method of UAV emergency disposition training based on fault tree analysis and snapshot

Science and Technology on Aircraft Control Laboratory, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

摘要

摘要: 针对无人机操作手在执行任务时不能有效地识别与处置特情的难题，提出了一种基于故障树遍历分析和快照技术的无人机特情模拟训练方法，并以样例无人机为研究案例进行了关键技术解析、通用化特情训练系统设计实现和试验.利用故障树理论，建立完备的无人机故障树模型，分析了需重点模拟训练的特情.利用快照技术，实现了不同飞行阶段训练的解耦；结合RTW（Real-Time Workshop）技术、数据库技术和视景仿真技术，完成了通用化无人机特情训练系统的开发，并成功应用于某型无人机的特情训练.给出了样例无人机操作手在不同方法下的训练结果，通过统计数据对比，表明该方法与常规的方法相比，训练效率更高，训练的完备度更好.
- 无人机 /
- 特情训练 /
- 故障树分析 /
- 快照技术 /
- 通用化
Abstract: In order to improve UAV (unmanned aerial vehicle) operators-ability to detect and deal with emergency cases, a method based on FTA (fault tree analysis) and software snapshot technology was proposed. Taking a flying-wing UAV for example, some key technologies were researched. Then a universalized emergency disposition training system was designed and hands-on trainings were carried out by degrees. The fault tree analysis method was used to build a relatively complete model of the reasons which could cause accidental cases, and key reasons were picked out for more intense trainings. The snapshot technology was introduced to decouple different training stages. Then with the RTW (real-time workshop) method, data base technology and VR(visual reality) technology, a universalized UAV emergency disposition training system was built and applied to the training of the target UAV. Through the comparative analysis, the effectiveness and high fault coverage of the method were verified.
- UAV(unmanned aerial vehicle) /
- emergency disposition training /
- fault tree analysis /
- snapshot /
- universalized

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参考文献(10)

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