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

该研究项目与俄亥俄州自然资源部（林业部）合作，重点研究在危险环境中操作的自主无人机系统（UAS），以在规定的燃烧期间进行野火监测，以预防和减轻火灾。 美国的气候变化加剧了野火，并加强了自然资源部的应对活动。来自森林管理和生态学、不确定性量化、传感器融合以及数据驱动建模和控制领域的专家合作，在非结构化、不确定和危险的火灾环境中部署自主空中机器人系统。这些合作的研究有助于荒地城市规划，准备和维持安全的荒地火灾响应;特别是，这项研究有助于了解温带硬木森林的地形，大气和森林燃料因素如何影响火灾强度和蔓延速度。 该项目邀请并鼓励学生参与机器人研究。通过其推广活动，该项目还向公众宣传机器人研究对应对社会挑战的价值。在这项工作中开发的理论，计算和实验方法和材料增强了态势感知，并在非结构化和不确定的危险环境中实现自主风险意识决策。UAS路径规划将制定和解决新的资源机会约束优化问题。UAS将通过Koopman算子理论实现物理信息学习，从而绕过计算繁重的工作，及时生成微观层次的局部条件。将设计新的传感器置信函数，准确地反映与火灾环境相关的假设中所包含的感知无知。证据信息融合将有效地处理传感器认知的不确定性，并允许在并非所有数据都可信的环境中进行可靠的集成。数据驱动控制将通过使用所应用的输入和所观察的输出的可用知识来使具有不确定动态的自动驾驶车辆在真实的时间内高效且可靠地操作，以学习未知输入，即使没有先前的训练数据或持续激励。通过扰动观测器获得的作用于UAS的扰动力和扭矩的实时估计值将提供有关荒地火灾区域周围湍流和气流的信息。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.