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Modelling Safe Landing Zone Detection Options to Assist in Safety Critical UAV Decision Making

机译:建模安全着陆区检测选项,协助安全关键无人机决策

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Unmanned Aerial Vehicles (UAVs) have the potential to significantly enhance current practice for a range of applications including emergency response in the aftermath of natural disasters and crowd surveillance during major sporting events. However, before this potential can be realized there are a number of safety related technical challenges which must be addressed including provision of a Safe Landing Zone (SLZ) detection algorithm which would be executed in the event of a UAV emergency. In the event of such an emergency a key consideration of any safety related algorithm is remaining flight time which can be influenced by battery life and weather conditions. Therefore within this paper we present preliminary work in modelling the execution time of three SLZ detection options, one of which incorporates a human-in-the-loop. While it may be desirable to always involve a human-in-the-loop in decision making concerning the optimal SLZ, this, and alternative options involving collaboration with other UAVs may not be feasible given the constraint of remaining flight time. The models discussed are subsequently used in conjunction with an estimate of the UAV's remaining flight,time to assist in autonomous decision making upon occurrence of a safety critical event.
机译:无人驾驶飞行器(无人机)有可能显着提高一系列应用程序的当前实践,包括在主要体育赛事中的自然灾害和人群监测中的应急响应。然而,在这种潜力可以实现之前,存在许多安全相关的技术挑战,必须解决,包括提供安全起落区(SLZ)检测算法,该算法将在UAV紧急情况下执行。如果发生这种紧急情况,则任何安全相关算法的关键考虑都是可能受电池寿命和天气条件影响的飞行时间。因此,在本文中,我们在建模三个SLZ检测选项的执行时间来提出初步工作,其中一个包含一个循环的人。虽然可能希望始终涉及关于最佳SLZ的决策中的人在循环中,但是,鉴于剩余飞行时间的约束,涉及与其他无人机的协作的替代选项可能是不可行的。讨论的模型随后与UAV剩余飞行的估计结合使用,以协助发生安全关键事件的自主决策。

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