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

– 自主飞行消防毯 –：用于消防应用的多无人机协作编队的新自适应和学习架构

基本信息

批准号：
2131802
负责人：
Xu Jin
金额：
$ 28.91万
依托单位：
University of Kentucky Research Foundation
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 2024-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2131802&HistoricalAwards=false
关键词：
Autonomous Flying Fire Blanket New

项目摘要

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.

火灾每年在美国造成重大损失。无人驾驶飞行器在消防和预防任务中的应用越来越多。然而，目前的消防飞行器存在一些局限性和制约因素，包括平台设计复杂，储罐容量和抑制液流量有限，以及成本高。该项目旨在开发一种新的“自动飞行灭火毯”系统，使用一组相对简单和廉价的无人机来协同系住灭火毯，并将其投放到指定地点。该系统对无人机团队的安全、精确和弹性操作提出了很高的要求。该项目的智力优势包括新的自适应和学习合作编队架构设计，将显著推进当前最先进的合作控制算法。该项目的更广泛影响包括与行业、我们的列克星敦消防部门和肯塔基州林业部门在系统的设计和验证方面的合作，以及在现实世界的消防和预防方面推广该系统。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。本课题的研究成果，将开设一门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)满足与时间和路径相关的多个地层约束要求；(2)在时间域和迭代域的不同运行条件下的适应和学习；(3)面对火灾现场潜在故障的弹性设计。当前的协同控制算法主要关注恒定或时变约束要求，这通常需要更激进的运动学行为，可能会导致驱动饱和。约束要求的几何和空间性质常常被忽视，很大程度上是由于将时域系统运动学与路径域约束相结合的困难。此外，现有的作品最多只能处理时间轴或迭代轴上的适应或学习。在时间和迭代领域中学习和适应的统一结构在文献中没有提到。此外，现有的协同编队算法往往忽略了物理上相互连接的智能体在受限操作过程中的故障。为了应对这些挑战，我们将使用势垒Lyapunov分析和复合能量函数讨论的新框架，基于通用势垒函数、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合作形成架构，同时考虑未知的外部有效载荷、系统非线性和外部干扰。该奖项反映了美国国家科学基金会的法定使命，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。