CAREER: Robust and Lightweight Formal Methods for Mobile Robot System Development

职业：用于移动机器人系统开发的稳健且轻量级的形式化方法

• 批准号: 2338706
• 负责人: John-Paul Ore
• 金额: $ 59.47万
• 依托单位: North Carolina State University
• 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: 2024-07-01 至 2029-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2338706&HistoricalAwards=false
• 关键词: CAREER; Robust; Lightweight; Formal; Methods

Mobile robots have the potential to amplify people's capabilities and reduce the amount of dull, dirty, or dangerous work that people currently do by hand across a broad set of human endeavors, especially in health care, national defense, construction, and agriculture. However, building a mobile robot is challenging and expensive because robot system developers struggle to cost-effectively build, test, and maintain reliable software. There is a gap in our knowledge of the robotic system development process that hinders building software tooling to support that process. With current tools and techniques, it is especially hard to build a prototype robot that can be upgraded into a safe, reliable, and dependable mobile robot without having to start from scratch. This project aims to create lightweight techniques and software tools that help robot system developers build safer and more capable robots while remaining economically feasible. This work leverages advances in video game development tools to make it easier to test what a mobile robot should and should not do. Building mobile robots requires expertise in several areas, including electrical, mechanical, and software engineering, and coordinating information across these areas can be challenging. In addition to building special software tools, this project puts research and teaching together so that recent advances in engineering, called model-based engineering, can prepare students to be part of an interdisciplinary future workforce that can build, operate, and maintain more reliable mobile robots.This project utilizes model checking of behavior trees and abstract type inference of physical units to automatically suggest system tests and to help ensure the absence of certain classes of software defects. Automated program analysis and testing are fundamental parts of modern software's continuous integration and deployment (CI/CD), but robot developers struggle to automate, track, and assess system testing efforts. Current techniques fall short of the demands of heterogeneous co-evolution of software/hardware systems, especially when systems are validated in the field. The project aims to develop techniques for inferring robotic system test cases from existing artifacts that robot system developers are already using. Additionally, this project seeks to create techniques for inferring and comparing groups of physical units that are commonly used together to better understand programs and discover novel patterns in how physical units are used in robotic system implementations. The expected outcomes of this project include creating a software tool that helps developers detect and track the physical units that co-occur in their software, creating a tool that helps robot system developers measure how much of a behavior tree their system has exercised, and developing test case prioritization strategies for field testing of mobile robotic systems. This research applies notions of model-based test coverage from software engineering to robot autonomy, and a goal of this research area is to lower the barriers to applying formal methods to robotic software systems.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.

移动机器人有潜力增强人们的能力，减少人们目前在广泛的人类活动中手工从事的枯燥、肮脏或危险的工作量，特别是在医疗保健、国防、建筑和农业领域。然而，构建移动机器人具有挑战性且成本高昂，因为机器人系统开发人员难以经济高效地构建、测试和维护可靠的软件。我们对机器人系统开发过程的了解存在差距，这阻碍了构建支持该过程的软件工具。利用当前的工具和技术，构建一个原型机器人尤其困难，并且无需从头开始就可以升级为安全、可靠和可靠的移动机器人。该项目旨在创建轻量级技术和软件工具，帮助机器人系统开发人员构建更安全、功能更强大的机器人，同时保持经济可行性。这项工作利用视频游戏开发工具的进步，使测试移动机器人应该做什么和不应该做什么变得更容易。构建移动机器人需要多个领域的专业知识，包括电气、机械和软件工程，而协调这些领域的信息可能具有挑战性。除了构建特殊的软件工具外，该项目还将研究和教学结合在一起，以便工程领域的最新进展（称为基于模型的工程）可以帮助学生成为跨学科未来劳动力的一部分，从而能够构建、操作和维护更可靠的移动设备机器人。该项目利用行为树的模型检查和物理单元的抽象类型推断来自动建议系统测试并帮助确保不存在某些类别的软件缺陷。自动化程序分析和测试是现代软件持续集成和部署 (CI/CD) 的基本组成部分，但机器人开发人员很难实现自动化、跟踪和评估系统测试工作。当前的技术无法满足软件/硬件系统异构协同进化的需求，特别是当系统在现场进行验证时。该项目旨在开发从机器人系统开发人员已经使用的现有工件中推断机器人系统测试用例的技术。此外，该项目旨在创建用于推断和比较通常一起使用的物理单元组的技术，以更好地理解程序并发现在机器人系统实现中如何使用物理单元的新模式。该项目的预期成果包括创建一个软件工具，帮助开发人员检测和跟踪在其软件中同时出现的物理单元，创建一个工具，帮助机器人系统开发人员测量他们的系统已经执行了多少行为树，以及开发用于移动机器人系统现场测试的测试用例优先级策略。这项研究将基于模型的测试覆盖范围的概念应用于从软件工程到机器人自主性，该研究领域的目标是降低将正式方法应用于机器人软件系统的障碍。该奖项反映了 NSF 的法定使命，并被认为是值得的通过使用基金会的智力优势和更广泛的影响审查标准进行评估来提供支持。