CPS: Synergy: Collaborative Research: Autonomy Protocols: From Human Behavioral Modeling to Correct-By-Construction, Scalable Control

CPS：协同：协作研究：自主协议：从人类行为建模到构建纠正、可扩展控制

• 批准号: 1624328
• 负责人: Behcet Acikmese
• 金额: $ 51.55万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1624328&HistoricalAwards=false
• 关键词: CPS; Synergy; Collaborative; Research; Autonomy

Computer systems are increasingly coming to be relied upon to augment or replace human operators in controlling mechanical devices in contexts such as transportation systems, chemical plants, and medical devices, where safety and correctness are critical. A central problem is how to verify that such partially automated or fully autonomous cyber-physical systems (CPS) are worthy of our trust. One promising approach involves synthesis of the computer implementation codes from formal specifications, by software tools. This project contributes to this "correct-by-construction" approach, by developing scalable, automated methods for the synthesis of control protocols with provable correctness guarantees, based on insights from models of human behavior. It targets: (i) the gap between the capabilities of today's hardly autonomous, unmanned systems and the levels of capability at which they can make an impact on our use of monetary, labor, and time resources; and (ii) the lack of computational, automated, scalable tools suitable for the specification, synthesis and verification of such autonomous systems.The research is based on study of modular reinforcement learning-based models of human behavior derived through experiments designed to elicit information on how humans control complex interactive systems in dynamic environments, including automobile driving. Architectural insights and stochastic models from this study are incorporated with a specification language based on linear temporal logic, to guide the synthesis of adaptive autonomous controllers. Motion planning and other dynamic decision-making are by algorithms based on computational engines that represent the underlying physics, with provision for run-time adaptation to account for changing operational and environmental conditions. Tools implementing this methodology are validated through experimentation in a virtual testing facility in the context of autonomous driving in urban environments and multi-vehicle autonomous navigation of micro-air vehicles in dynamic environments. Education and outreach activities include involvement of undergraduate and graduate students in the research, integration of the research into courses, demonstrations for K-12 students, and recruitment of research participants from under-represented demographic groups. Data, code, and teaching materials developed by the project are disseminated publicly on the Web.

在安全性和正确性至关重要的情况下，计算机系统越来越依赖于增强或取代人类操作员来控制诸如运输系统、化工厂和医疗设备之类的环境中的机械设备。一个核心问题是如何验证这种部分自动化或完全自主的网络物理系统（CPS）值得我们信任。一个有前途的方法涉及合成的计算机实现代码从正式规格，软件工具。该项目有助于这种“正确的建设”的方法，通过开发可扩展的，自动化的方法与可证明的正确性保证的控制协议的合成，基于人类行为模型的见解。其目标是：（i）当今几乎不自主的无人系统的能力与它们能够影响我们使用金钱、劳动力和时间资源的能力水平之间的差距;以及（ii）缺乏适用于规范的计算的、自动的、可扩展的工具，这种自治系统的综合和验证。这项研究是基于模块化强化学习的研究-基于人类行为的模型，通过实验得出人类如何在动态环境中控制复杂交互系统的信息，包括汽车驾驶。从这项研究的架构见解和随机模型与规范语言的基础上线性时序逻辑，指导自适应自主控制器的合成。运动规划和其他动态决策是通过基于代表底层物理的计算引擎的算法进行的，并提供运行时自适应以应对不断变化的操作和环境条件。实现这种方法的工具进行验证，通过实验在一个虚拟的测试设施的背景下，在城市环境中的自动驾驶和多车辆自主导航的微型飞行器在动态环境中。教育和推广活动包括本科生和研究生参与研究，将研究纳入课程，为K-12学生进行演示，以及从代表性不足的人口群体中招募研究参与者。该项目开发的数据、代码和教材在网上公开传播。