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

未经授权和恶意无人机的威胁越来越大，研究和行业团体正在寻找解决方案。然而，目前的反无人机技术往往容易失败，成本效益不高，或者可能影响附近的合法飞机。该提案开发了一种多无人机无人机捕获网（many -Net）系统，该系统使用一组低成本的自主无人机协同系好捕获网以捕获目标无人机。该新系统采用基于学习的协作编队架构设计，具有环境感知自适应安全约束，对无人机团队的安全操作提出了很高的要求。该项目的更广泛影响包括(a)面向高中工程预科学生的概念设计竞赛，(b)涉及女性和代表性不足的学生的机器人捕获游戏竞赛，(c)面向K-12教师的机器人程序，(d)促进本科教学中的探索性学习任务，(e)关于安全关键型智能多代理系统的新研究生课程，(f)以及与行业合作伙伴的合作，以促进研究开发。验证、评估和技术转让。本项目旨在为基于学习的协同控制理论和新的环境感知自适应安全分析做出基础性贡献。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)对于多智能体系统，所考虑的多个安全要求可能相互冲突，也可能与系统初始状态相冲突。现有的多智能体系统安全关键控制算法只能处理恒定或时变的安全集，不能动态适应环境，也不能处理安全冲突。该研究探讨了基于学习的协同控制体系结构，以解决多智能体系统的环境感知自适应安全要求。提出了一种集成了协同动态深度神经网络的集成障碍函数结构，用于学习多维环境参数和未知目标速度的动态特性。安全冲突是通过将初始状态和虚拟障碍整合到集成障碍函数中来解决的，并结合指示器函数来修改不太关键的（“软”）安全集，以保证更关键的（“硬”）安全要求。所提出的体系结构广泛适用于在复杂环境中运行的多智能体系统的许多应用程序。该项目由电气、通信和网络系统部（ECCS）和促进竞争研究的既定计划（EPSCoR）共同资助。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。

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.