Collaborative Research: NRI: Integration of Autonomous UAS in Wildland Fire Management

合作研究：NRI：自主无人机在荒地火灾管理中的整合

基本信息

批准号：
2132799
负责人：
Amit Sanyal
金额：
$ 53.7万
依托单位：
Syracuse University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-01-01 至 2025-12-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2132799&HistoricalAwards=false
关键词：
Collaborative Research NRI Integration Autonomous

项目摘要

This research project, in cooperation with the Ohio Department of Natural Resources (Division of Forestry), focuses on autonomous unmanned aerial systems (UAS) for operations in hazardous environments to perform wildfire monitoring during prescribed burns for fire prevention and mitigation. Climate change in the US has exacerbated wildfires and intensified the Department of Natural Resources activities in response. Experts from the areas of forest management and ecology, uncertainty quantification, sensor fusion and data-driven modeling and control collaborate to deploy autonomous aerial robotic systems in unstructured, uncertain, and hazardous fire environments. The research from these collaborations aids wildland-urban planning, preparing for and sustainment of a safe wildland fire response; in particular, this research contributes to understanding how topographic, atmospheric and forest fuel factors in temperate hardwood forests influence fire intensity and rate of spread. This project invites and encourages students to participate in robotics research. Through its outreach activities, the project also informs the general public of the value of robotics research for addressing societal challenges.Theoretical, computational, and experimental methods and materials developed in this work enhance situational awareness and enables autonomous risk-aware decision-making in unstructured and uncertain hazardous environments. UAS path planning will formulate and solve novel resource chance-constrained optimization problems. UAS will bypass computational heavy lifting to generate in-time micro-level local conditions by enabling physics-informed learning through Koopman operator theory. New sensor belief functions will be designed that accurately reflect sensing ignorance contained in hypotheses related to the fire environment. Evidential information fusion will effectively handle sensor epistemic uncertainty and allow reliable integration in an environment where not all data is trustworthy. Data-driven control will enable efficient and reliable operation of autonomous vehicles with uncertain dynamics in real time by using available knowledge of applied inputs and observed outputs, to learn the unknown inputs even without prior training data or persistent excitation. Real-time estimates of disturbance forces and torques acting on an UAS obtained by the disturbance observer will provide information on the turbulence and air flow around a wildland fire region.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该研究项目与俄亥俄州自然资源部（林业司）合作，着重于在危险环境中进行自动无人空中系统（UAS）进行操作，以在规定的烧伤中进行野火监测，以进行防火和缓解。美国的气候变化加剧了野火，并加剧了自然资源部活动的回应。来自森林管理和生态学领域的专家，不确定性量化，传感器融合以及数据驱动的建模和控制协作，以在非结构化，不确定和危险的火灾环境中部署自动空中机器人系统。这些合作的研究有助于野生世界的规划，为安全的野外火灾响应做准备和维持；特别是，这项研究有助于了解温带硬木森林中的地形，大气和森林燃料因子如何影响火力强度和传播速度。该项目邀请并鼓励学生参加机器人研究。通过其外展活动，该项目还将机器人研究对应对社会挑战的价值告知公众。在这项工作中开发的理论，计算和实验方法和材料增强了情境意识，并使无自动风险的决策在非结构化和不确定的危险环境中具有自主性风险意识。 UAS路径计划将制定和解决新颖的资源机会约束优化问题。 UAS将通过Koopman操作员理论启用物理学的学习来绕过计算繁重的举重，从而产生微型级别的本地条件。将设计新的传感器信念功能，以准确反映与火灾环境有关的假设中包含的感应无知。证据信息融合将有效地处理传感器认知不确定性，并允许在并非所有数据都值得信赖的环境中可靠的整合。数据驱动的控制将通过使用应用输入和观察到的输出的可用知识来实时对具有不确定动态的自动驾驶汽车的有效和可靠的操作，即使没有事先培训数据或持续激发，也可以学习未知输入。对骚乱观察者获得的UA的干扰力和扭矩的实时估计将提供有关野生火灾地区周围的湍流和空气流的信息。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的审查标准来通过评估来通过评估来支持的。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Development of a Free-Flight Wind Test Facility Featuring a GNSS Simulator to Achieve Immersive Drone Testing

开发具有 GNSS 模拟器的自由飞行风测试设施，以实现沉浸式无人机测试

DOI：
10.2514/6.2022-2052
发表时间：
2022
期刊：
AIAA SCITECH 2022 Forum
影响因子：
0
作者：
Catry, Guillaume;Thurling, Andy;Bosson, Nicolas;Dzodic, Aleksandar;Le Porin, Peter;Wang, Ningshan;Sanyal, Amit K.;Noca, Flavio;Glauser, Mark N.
通讯作者：
Glauser, Mark N.

Input Influence Matrix Design for MIMO Discrete-Time Ultra-Local Model

MIMO离散时间超局部模型的输入影响矩阵设计