CAREER: Towards Environment-Aware Adaptive Safety for Learning-Enabled Multiagent Systems with Application to Target Drone Capturing

职业：为支持学习的多智能体系统实现环境感知的自适应安全，并应用于目标无人机捕获

• 批准号: 2336189
• 负责人: Xu Jin
• 金额: $ 54.27万
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-15 至 2029-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2336189&HistoricalAwards=false
• 关键词: CAREER; Towards; Environment; Aware; Adaptive

There are increasing threats from unauthorized and malicious drones with research and industry communities looking for solutions. However, current anti-drone techniques are often prone to failure, not cost effective, or could affect legitimate nearby aircraft. This proposal develops a Multi-UAV Drone Catch Net (MUCH-Net) system that uses a team of low-cost autonomous unmanned aerial vehicles to collaboratively tether a catch net to capture the target drone. This new system puts a high demand on the safe operations of the aerial vehicle team using a learning-based cooperative formation architecture design with environment-aware adaptive safety constraints. The broader impacts of the project include (a) a concept design contest for high-school pre-engineering program students, (b) a robot capture game competition, which involve women and underrepresented students, (c) a robotic program for K-12 teachers, (d) promotion of exploratory learning assignments in undergraduate teaching, (e) a new graduate course on safety-critical intelligent multiagent systems, (f) and collaborations with industry partners to facilitate research development, verification, assessment, and technology transfer.This CAREER project aims to make fundamental contributions to theories of learning-based cooperative control with new environment-aware adaptive safety analysis. Major technical challenges include: (a) due to the complex operating environment, the safety considerations are environment-aware and adaptive; and (b) for multiagent systems, the multiple safety requirements considered can be conflicting with each other or with the initial system state. Existing safety-critical control algorithms for multiagent systems only address constant or time-varying safety sets, which cannot dynamically adapt to the environment, and cannot address safety conflicts. The research investigates learning-based cooperative control architectures to address environment-aware adaptive safety requirements for multiagent systems. An integrated barrier function structure that integrates a cooperative dynamic deep neural network to learn the dynamics of a multi-dimension environment parameter and unknown target velocity is proposed. Safety conflicts are addressed by integrating initial state and virtual barriers into the integrated barrier functions, with indicator functions incorporated to modify the less critical (“soft”) safety sets, in order to guarantee the more critical (“hard”) safety requirements. The proposed architectures are widely applicable to many applications with multiagent systems operating in complex environments. This project is jointly funded by the Electrical, Communications and Cyber Systems Division (ECCS) and the Established Program to Stimulate Competitive Research (EPSCoR).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.

研究和行业社区正在寻找解决方案，未经授权和恶意无人机的威胁越来越大。但是，当前的反无人机技术通常容易发生故障，而不是成本效益，或者可能会影响飞机附近的合法性。该提案开发了一个多动力无人机捕获网（漫长的网络）系统，该系统使用一个低成本的无人驾驶飞机团队来协作捕获捕获网以捕获目标无人机。这个新系统使用基于学习的协调形成架构设计对航空车辆团队的安全运营提出了很大的需求，并具有环境感知的自适应安全限制。该项目的广播公司的影响包括（a）针对高中预科课程学生的概念设计竞赛，（b）机器人捕获游戏竞赛，涉及妇女和代表性不足的学生，（​​c）为K-12教师的机器人计划，（c）（c）（d）促进探索性学习范围的跨性毕业生和跨性别的毕业生，（e）（e）（e）（e）（e）（e）（e）（e）（e）（e）（e）（e）支持研究开发，验证，评估和技术转移。本职业项目旨在通过新的环境感知的自适应安全分析为基于学习的合作控制理论做出基本贡献。主要的技术挑战包括：（a）由于复杂的操作环境，安全考虑是环境意识和适应性的； （b）对于多种系统，所考虑的多个安全要求可能相互冲突，也可能与初始系统状态发生冲突。现有的针对多基因系统的现有安全控制算法仅解决无法动态地适应环境的恒定或时变安全集，并且无法解决安全冲突。该研究调查了基于学习的合作控制体系结构，以满足环境感知的自适应安全要求。提出了一个集成了协调的动态深中性网络的集成屏障函数结构，以了解多维环境参数和未知目标速度的动力学。通过将初始状态和虚拟障碍集成到集成的屏障功能中来解决安全冲突，并结合了指标功能来修改较少的关键（“软”）安全集，以确保更关键（“硬”）安全要求。所提出的体系结构广泛适用于许多在复杂环境中运行的多种系统的应用程序。该项目由电气，通信和网络系统部（ECC）共同资助，并启用竞争性研究的既定计划（EPSCOR）。该奖项反映了NSF的法定任务，并通过基金会的知识分子优点和更广泛的影响标准通过评估来诚实地对支持。