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Initial designs for an automatic forced landing system for safer inclusion of small unmanned air vehicles into the national airspace

机译:自动强制着陆系统的初步设计,可将小型无人驾驶飞机更安全地纳入国家领空

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Small unmanned air vehicles (UAVs) have unique advantages and limitations which will affect their safe inclusion into the national airspace system. In particular, challenges associated with emergency handling in beyond line of sight operations will be especially critical to address. This paper proposes initial designs for an autonomous decision system for UAVs to select emergency landing sites in a vehicle fault scenario. The overall design consists of two main components: pre-planning and realtime optimization. In the pre-planning component, the system uses offline information such as geographical and population data to generate landing loss maps over the operating environment, which can be used to constrain planning of flight routes to satisfy a bound on the expected landing loss under worst-case fault. In the real-time component, onboard sensor data is used to update a probabilistic risk assessment for potential landing areas allowing for refinement of the expected loss calculation and landing site selection at the time of a fault. The mathematical models and computational algorithms constituting these system components are based upon methodologies in optimal control and statistical inference. Simulation results are provided to demonstrate the application of the proposed algorithms in an example of quadrotor emergency landing over a section of UC Berkeley campus.
机译:小型无人飞行器(UAV)具有独特的优势和局限性,这将影响其安全地纳入国家空域系统。尤其是在视线范围之外的紧急处理方面,所面临的挑战将特别重要。本文提出了用于无人机的自主决策系统的初始设计,以在车辆故障情况下选择紧急着陆点。总体设计包括两个主要部分:预计划和实时优化。在预计划组件中,系统使用离线信息(例如地理和人口数据)来生成运行环境上的着陆损失图,该图可用于限制飞行路线的规划,从而满足最恶劣情况下对预期着陆损失的限制。案例错误。在实时组件中,机载传感器数据用于更新潜在着陆区的概率风险评估,从而可以在发生故障时完善预期损失的计算和着陆点的选择。构成这些系统组件的数学模型和计算算法基于最佳控制和统计推断中的方法。提供了仿真结果,以证明所提出的算法在加州大学伯克利分校校园某部分的四旋翼紧急着陆示例中的应用。

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