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S&AS: FND: Safe Task-Aware Autonomous Resilient Systems (STAARS)

基本信息

批准号：
1724248
负责人：
Atilla Dogan
金额：
$ 54.98万
依托单位：
University of Texas at Arlington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1724248&HistoricalAwards=false
关键词：
S&amp FND Safe Task Aware

项目摘要

Realizing the full potential of unmanned aerial systems (UAS) for commercial and societal benefits will call for autonomous UAS that must operate around people, especially urban/suburban areas, and respect safety, privacy, and regulatory concerns. This project will quantify the operations of UAS executing a task in urban/suburban environment as "risk" to these considerations, then develop dynamic risk assessment and guidance algorithms to compute least risky trajectories for the UAS to follow while executing a task. The project will produce knowledge on the proper autonomy level of UAS in urban operations, and will benefit FAA (Federal Aviation Administration) in UAS regulatory issues. The technological advances in this project will also contribute to multiple fields including autonomy, mobile networking, and intelligent control. The task & risk-aware decision-making framework developed in this project can be applied not only to UAS, but also to other unmanned systems on the ground and in/under water, with broad applications in smart health, transportation, and manufacturing domains. The PIs also expect that successful demonstrations of "safe" UAS operation in various risk conditions will increase the public's acceptance of UAS technology, and benefit broad commercial UAS use and job market. The project will also produce exciting learning and training opportunities for students and the community at large to learn UAS technologies.The project will use two raster maps to quantify risk: (1) PREM (Probability Risk Exposure Map) defines the risk of exposure of people and property on the ground to the presence of a UAS in the air as a function of position and time. (2) PURM (Probabilistic UAS Reachability Map) is the probability that the UAS can reach a position on the ground from its current position, computed based on the vehicle's capability (both nominal and diminished by possible failures) and environmental conditions such as wind. Defining the PURM by joint probability of reachability domains for nominal and all failure cases results in a resilient system, since decisions are made considering all possible operational modes. Trajectory planning in the PURM will use a modified, bidirectional, probabilistic RRT (Rapidly Expanding Random Tree) to efficiently, incrementally plan a set of trajectories that minimizes the overall risk. An autonomous decision algorithm then can keep the risk below an acceptable level as it guides the UAS in the successful completion of a given task. Because the safe operation of UAS also highly relies on effective communication among UAS and between UAS and a Command and Control Center, the project will also develop a decentralized dynamic communication schemes under different risk levels and mobility constraints.

实现无人机系统（UAS）的商业和社会效益的全部潜力将需要自主的UAS，它必须在人们周围运行，特别是城市/郊区，并尊重安全，隐私和监管问题。该项目将量化UAS在城市/郊区环境中执行任务的操作作为这些考虑的“风险”，然后开发动态风险评估和引导算法来计算UAS在执行任务时遵循的风险最小的轨迹。该项目将产生关于UAS在城市运营中的适当自主水平的知识，并将使FAA（联邦航空管理局）在UAS监管问题上受益。该项目的技术进步也将有助于多个领域，包括自主，移动的联网和智能控制。本项目开发的任务风险感知决策框架不仅可以应用于无人机系统，还可以应用于地面和水中/水下的其他无人系统，在智能健康，交通和制造领域具有广泛的应用。PI还预计，在各种风险条件下成功演示“安全”的UAS操作将提高公众对UAS技术的接受度，并有利于广泛的商业UAS使用和就业市场。该项目还将为学生和整个社区提供令人兴奋的学习和培训机会，以学习UAS技术。该项目将使用两个栅格地图来量化风险：（1）PREM（概率风险暴露地图）定义了地面人员和财产暴露于空中UAS的风险，作为位置和时间的函数。 (2)PURM（ProbabilityUAS Reachability Map）是UAS从其当前位置到达地面位置的概率，基于车辆的能力（标称和可能的故障减少）和环境条件（如风）计算。定义PURM的联合概率的可达域的名义和所有故障的情况下，在一个弹性系统的结果，因为决策考虑所有可能的操作模式。PURM中的轨迹规划将使用修改的双向概率RRT（快速扩展随机树）来有效地增量规划一组轨迹，以最大限度地降低总体风险。然后，自主决策算法可以将风险保持在可接受的水平以下，因为它引导UAS成功完成给定任务。由于无人机系统的安全运行也高度依赖于无人机系统之间以及无人机系统与指挥控制中心之间的有效通信，因此该项目还将开发不同风险水平和移动性约束下的分散动态通信方案。