Collaborative Research: Towards Attack-Resilient Vision-Guided Unmanned Aerial Vehicles: An Observability Analysis Approach

合作研究：迈向抗攻击视觉引导无人机：一种可观测性分析方法

基本信息

批准号：
2137753
负责人：
Hamidreza Jafarnejadsani
金额：
$ 30.98万
依托单位：
Stevens Institute of Technology
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：
2022-05-15 至 2025-04-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2137753&HistoricalAwards=false
关键词：
Collaborative Research Towards Attack Resilient

项目摘要

This grant will fund research that improves the resilience of future mobile robotic technologies to malicious cyber-attacks for safety-critical applications in transportation, aerospace, and military systems, thereby promoting the progress of science, advancing prosperity, and securing the national defense. Unmanned aerial vehicles and other mobile robots rely on image streams from onboard cameras, as well as communication with other autonomous systems, to perform cooperative tasks such as navigation and collision avoidance. Vulnerabilities in networked vision-guided systems can be exploited using powerful machine-learning methods to launch adversarial attacks that compromise function, endanger human lives, and damage property. This research project advances a new foundational framework for detecting and responding to stealthy and malicious cyber-attacks that simultaneously target mission planning, control, perception, and sensor data. This framework will enhance the reliability of vision-guided autonomous systems such as self-driving cars and networked aerial vehicles, hardening these against cyber threats and malicious actions. Efforts aimed at achieving broader impact include integration of research activities in project-based graduate courses, as well as engagement of undergraduate students in faculty-mentored summer research projects that motivate their interest in STEM, graduate education, and careers in high-tech industries. Outreach programs will use mobile robotic demonstration platforms to inspire K-12 students to pursue engineering-related education paths.This research aims to make fundamental and rigorous contributions to the use of control theory and machine-learning tools for characterizing and defending against stealthy attacks that exploit vulnerabilities in vision-guided, networked autonomous systems, with particular emphasis on high-dimensional visual data. It achieves this outcome by developing an online learning framework able to synthesize control policies from image frames in real-time, without current restrictions to offline implementations that fail to include system dynamics in the control loop. For multi-agent vision-guided dynamical systems, this research provides a novel and holistic framework that characterizes stealthy attacks based on the unobservable subspaces of both the physical system dynamics and the neural network model used for perception. Using stochastic optimization and simulation, the attack detection methodology extends the existing model-based observer methods for linear time-invariant systems to deal with stealthy attacks against a networked system with autonomous agents represented by general time-varying and nonlinear models. Evaluation of the theoretical framework relies on experiments with quadcopters in an indoor laboratory environment, as well as the use of advanced simulation software.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.

这笔赠款将资助研究，提高未来移动机器人技术对交通、航空航天和军事系统中安全关键应用的恶意网络攻击的抵御能力，从而促进科学进步、促进繁荣和确保国防安全。无人机和其他移动机器人依靠机载摄像头的图像流以及与其他自主系统的通信来执行导航和避免碰撞等协作任务。可以使用强大的机器学习方法来利用网络视觉引导系统中的漏洞来发起对抗性攻击，从而损害功能、危及人类生命和损坏财产。该研究项目提出了一种新的基础框架，用于检测和响应隐形和恶意网络攻击，同时针对任务规划、控制、感知和传感器数据。该框架将提高自动驾驶汽车和联网飞行器等视觉引导自主系统的可靠性，增强这些系统抵御网络威胁和恶意行为的能力。旨在实现更广泛影响的努力包括将研究活动整合到基于项目的研究生课程中，以及让本科生参与教师指导的夏季研究项目，以激发他们对 STEM、研究生教育和高科技行业职业的兴趣。推广计划将使用移动机器人演示平台来激励 K-12 学生追求工程相关的教育道路。这项研究旨在为使用控制理论和机器学习工具来表征和防御隐秘攻击做出根本性和严格的贡献。利用视觉引导的网络自治系统中的漏洞，特别强调高维视觉数据。它通过开发一个在线学习框架来实现这一成果，该框架能够实时从图像帧合成控制策略，而不受当前无法在控制循环中包含系统动态的离线实现的限制。对于多智能体视觉引导动力系统，这项研究提供了一种新颖的整体框架，该框架基于物理系统动力学和用于感知的神经网络模型的不可观察子空间来表征隐秘攻击。使用随机优化和模拟，攻击检测方法扩展了现有的基于模型的线性时不变系统观察器方法，以处理针对具有由一般时变和非线性模型表示的自主代理的网络系统的隐形攻击。理论框架的评估依赖于室内实验室环境中的四轴飞行器实验以及先进模拟软件的使用。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持标准。