“Autonomous Flying Fire Blanket”: New Adaptive And Learning Architectures For Multi-UAV Cooperative Formation With Firefighting Applications

大火每年都会在美国造成重大破坏。无人机越来越多地被用于灭火和预防任务。然而，目前的消防飞行器受到一些限制和制约，包括平台设计复杂，抑制液的油箱容量和流量有限，以及成本较高。该项目旨在开发一种新的“自主飞行灭火毯”系统，利用一组相对简单和廉价的无人机团队，协同系绳一条灭火毯，将其投放到指定的地点。该系统对飞行器编队的安全、精确、弹性作业提出了很高的要求。该项目的智能优点包括新的自适应和学习协作编队结构设计，这将显著促进当前协作控制算法的最先进水平。该项目的更广泛影响包括与业界、我们的列克星敦消防局和肯塔基州林业部合作，设计和验证该系统，并促进该系统在现实世界中的灭火和预防。该项目将促进K-12和本科生的研究和教育经验，包括代表性不足的学生。根据该项目的研究成果，将开发一门关于智能控制方法的研究生课程。该项目的目标是为物理互联的无人机开发新的自适应和学习架构，以确保安全、精确和有弹性的操作。需要解决的主要技术挑战包括：(1)满足依赖于时间和路径的多个编队约束要求；(2)在时间域和迭代域上的变化操作条件下的适应和学习；以及(3)面对火灾现场潜在故障的弹性设计。目前的协同控制算法大多集中在恒定或时变的约束要求上，这往往需要更激进的运动学行为，这可能会导致驱动饱和。约束要求的几何和空间性质经常被忽视，这主要是因为在将时间域系统运动学与路径域约束相结合方面存在困难。此外，现有的工作最多只能处理时间轴或迭代轴上的适应或学习。在时间和迭代领域学习和适应的统一结构在文献中没有涉及。此外，现有的协同编队算法经常忽略受限操作期间物理上互连的代理的任何故障。为了应对这些挑战，我们将使用障碍李亚普诺夫分析和新的复合能量函数讨论框架，基于通用障碍函数、自适应学习结构和弹性控制框架设计，开发新的自适应和学习协作编队结构，同时考虑未知外部有效载荷、系统非线性和外部干扰。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Fires cause significant damage in the United States every year. Unmanned aerial vehicles have been increasingly used in fire fighting and prevention tasks. However, current firefighting aerial vehicles suffer from several limitations and constraints, including complex designs of the platforms, limited tank capacity and flow rate for the suppressant fluid, and high costs. This project aims to develop a new “autonomous flying fire blanket” system, using a team of relatively simple and cheap unmanned aerial vehicles to collaboratively tether a fire blanket to be dropped at a designated place. This system puts a high demand on the safe, precise, and resilient operations of the aerial vehicle team. The intellectual merits of the project include new adaptive and learning cooperative formation architecture designs that will significantly advance the current state-of-the-art in cooperative control algorithms. The broader impacts of the project include collaboration with the industry, our Lexington Fire Department, and Kentucky Division of Forestry, on the designs and verifications of the system, and on promoting the system for real-world fire fighting and prevention. This project will advance research and educational experiences for K-12 and undergraduate students including underrepresented students. A graduate course on intelligent control methods will be developed based on the research findings of this project. The goal of this project is to develop new adaptive and learning architectures for physically interconnected unmanned aerial vehicles to ensure safe, precise, and resilient operations. Major technical challenges to be addressed include: (1) satisfying multiple formation constraint requirements that are time and path dependent; (2) adaptation and learning under varying operation conditions over both the time and iteration domain; and (3) resilient designs in the face of potential malfunctioning at the fire scene. Current cooperative control algorithms mostly focus on constant or time-varying constraint requirements, which often require more aggressive kinematic behavior that can potentially cause actuation saturation. The geometric and spatial nature of the constraint requirements is often overlooked, largely due to difficulties in integrating time-domain system kinematics with path-domain constraints. Moreover, existing works can at best deal with adaption or learning over either the time or iteration axis. Unified structures to learn and adapt over both the time and iteration domain have not been addressed in the literature. Furthermore, existing cooperative formation algorithms often ignore any malfunctioning of the physically interconnected agents during constrained operations. To address these challenges, we will use barrier Lyapunov analysis and a new framework of composite energy function discussion, to develop new adaptive and learning cooperative formation architectures based on universal barrier functions, adaptive learning structures, and resilient control framework designs, while taking unknown external payloads, system nonlinearities, and external disturbances into considerations.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.