“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

• 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

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.

每年美国大火造成严重损害。无人驾驶汽车越来越多地用于消防和预防任务。但是，当前正在消防的飞机遭受了几种局限性和限制，包括平台的复杂设计，抑制液的储罐容量和流速有限，成本高。该项目旨在使用一支相对简单且廉价的无人驾驶汽车的团队来开发一个新的“自动飞行火毯”系统，以合作将火毯束缚在指定的地方。该系统对空中车辆团队的安全，精确和抵抗力的运营提出了很高的需求。该项目的智力优点包括新的自适应和学习协调形成架构设计，这些设计将大大推动合作控制算法的当前最新技术。该项目的更广泛影响包括与行业，我们的列克星敦消防局和肯塔基州林业部的合作，对系统的设计和验证以及促进现实世界中消防和预防的系统。该项目将为K-12和本科生（包括代表性不足的学生）提供研究和教育经验。根据该项目的研究结果，将开发有关智能控制方法的研究生课程。该项目的目的是开发新的自适应和学习体系结构，以确保与物理相互联系的无人驾驶汽车，以确保安全，精确和抵抗力的操作。要解决的主要技术挑战包括：（1）满足依赖时间和路径的多个形成约束要求； （2）在时间和迭代域的不同操作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。当前的合作控制算法主要集中于恒定或随时间变化的约束要求，这通常需要更具侵略性的运动行为，这可能会引起动作满意度。约束要求的几何和空间性质通常被忽略，这主要是由于难以将时间域系统运动学与路径域约束整合。此外，现有作品充其量可以在时间或迭代轴上进行适应或学习。文献中尚未解决统一的学习和适应统一结构。此外，现有的协调形成算法通常会忽略在约束操作期间物理互连药物的任何故障。为了应对这些挑战，我们将使用屏障Lyapunov分析和复合能量功能讨论的新框架，以基于普遍的障碍功能，自适应学习结构，适应性的控制框架设计以及弹性的控制框架设计，开发新的适应性和学习协调形成架构，同时将未知的外部索要和外部障碍予以审查，并将其置于考虑的外部灾难，并授予nsf，以表现出色。利用基金会的知识分子和更广泛的影响审查标准。

项目成果

Attacker-Resilient Adaptive Path Following of a Quadrotor with Dynamic Path-Dependent Constraints

• DOI: 10.23919/acc55779.2023.10156266
• 发表时间: 2023-05
• 期刊: 2023 American Control Conference (ACC)
• 作者: Xu Jin;Zhongjun Hu

Adaptive formation control architectures for a team of quadrotors with multiple performance and safety constraints

• DOI: 10.1002/rnc.6824
• 发表时间: 2023-06
• 期刊: International Journal of Robust and Nonlinear Control
• 影响因子: 3.9
• 作者: Zhongjun Hu;Xu Jin

Formation Control for an UAV Team With Environment-Aware Dynamic Constraints

• DOI: 10.1109/tiv.2023.3295354
• 发表时间: 2024-01
• 期刊: IEEE Transactions on Intelligent Vehicles
• 影响因子: 8.2
• 作者: Zhongjun Hu;Xu Jin

Formation-Based Decentralized Iterative Learning Cooperative Impedance Control for a Team of Robot Manipulators

• DOI: 10.1109/tsmc.2022.3189661
• 发表时间: 2023-02
• 期刊: IEEE Transactions on Systems, Man, and Cybernetics: Systems
• 作者: Xu Jin

Constrained Load Transportation by A Team of Quadrotors

由一组四旋翼飞行器进行约束负载运输

• DOI: 10.1109/cdc51059.2022.9992662
• 发表时间: 2022
• 期刊: IEEE
• 作者: Jin, Xu;Hu, Zhongjun
• 通讯作者: Hu, Zhongjun