面向拥挤空域安全的自主无人机交通管理系统研究

Lanier A Watkins, Nick Sarfaraz, S. Zanlongo, Joshua Silbermann, T. Young, Randall Sleight
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引用次数: 8

摘要

目前,无人机系统(UAS)交通管理(UTM)是美国联邦航空管理局(FAA)对400英尺以下空中交通管理的愿景。生产UTM系统倾向于只驻留在专门的测试站点和操作中心。UTM已经被FAA作为一个操作概念(ConOps)加以阐述。UTM ConOps描述了UAS、UAS操作员和UTM系统本身之间的复杂交互。这些交互可能涉及人工操作,也可能是完全自动化的。目前,大多数UTM研究和实验原型都没有从端到端看待UTM概念;相反,他们专注于UTM的特定方面,因此无法探索和测试UTM生态系统的整体性能。同样重要的是确保生产UTM能够满足未来空域的需求,预计到2035年,每小时将有65,000架UAS操作(起飞和降落)。美国最繁忙的机场目前每小时处理300次航班。在本文中,我们使用一组用于飞行计划去冲突的自主算法来评估UTM系统的一部分。初步结果表明,用于路径规划、战略去冲突以及检测和避免(DAA)的自主算法能够扩展到高拥塞场景,同时大大减少无人机之间的碰撞,即使几乎所有无人机都偏离了去冲突计划(即流氓无人机)。我们还观察到,去冲突算法代表了分离层次结构中占主导地位的安全层,因为战略去冲突算法管理空域密度,尽管以更长的任务完成时间为代价。我们的测试是在MATLAB模拟器上完成的,该模拟器使用RRT*算法进行飞行规划,使用两种不同的调度程序(遗传算法和NASA Stratway战略冲突解决算法)进行战略冲突消除,以及用于DAA的小型无人机系统机载避撞系统(ACAS sXu)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Investigative Study Into An Autonomous UAS Traffic Management System For Congested Airspace Safety
Currently, Unmanned Aircraft System (UAS) Traffic Management (UTM) is the Federal Aviation Administration’s (FAA) vision for air traffic management below 400 feet. Production UTM systems tend to reside only at specialized test sites and operational centers. UTM has been articulated as a concept of operation (ConOps) by the FAA. The UTM ConOps describes a complex interaction between UAS, UAS Operators, and the UTM system itself. These interactions may involve human operators, or be fully automated. Currently, most UTM studies and experimental prototypes do not look at the UTM concept from end-to-end; instead, they focus on specific aspects of UTM and thus cannot explore and test the holistic performance of a UTM ecosystem. Equally important is ensuring that production UTM can scale to meet the demands of future airspace, which is estimated to be 65,000 UAS operations (takeoffs and landings) per hour by 2035. The busiest US airports currently handle 300 operations per hour.In this paper, we evaluate a portion of the UTM system using a set of autonomous algorithms for flight plan de-confliction. Preliminary results suggest that the autonomy algorithms used for path planning, strategic de-confliction, and detect and avoid (DAA) are capable of scaling to high-congestion scenarios while drastically reducing collisions between UAS, even with almost all UAS deviating from de-conflicted plans (i.e., rogue UAS). We also observed that de-confliction algorithms represent a dominating safety layer in the separation hierarchy, since the strategic de-confliction algorithms manage airspace density, albeit at the cost of longer mission completion times. Our testing was done using a MATLAB simulator, which used the RRT* algorithm for flight planning, two different schedulers (Genetic Algorithm and the NASA Stratway Strategic Conflict Resolution algorithm) for strategic de-confliction, and the Airborne Collision Avoidance System for small unmanned aircraft systems (ACAS sXu) for DAA.
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