CPS: Medium: Computation-Aware Autonomy for Timely and Resilient Multi-Agent Systems

CPS：中：及时且有弹性的多代理系统的计算感知自治

• 批准号: 1932074
• 负责人: Ryan Williams
• 金额: $ 119.77万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1932074&HistoricalAwards=false
• 关键词: CPS; Medium; Computation; Aware; Autonomy

We are entering an age of unprecedented access to information, where transformational methodologies are demonstrating a clear vision of an autonomy-driven future. Self-driving cars, precision agriculture, robotic monitoring, and infrastructure inspection are but a few areas experiencing an autonomy revolution. To continue in this promising direction, it is critical that we facilitate the safe and reliable coordination of diverse cyber-physical systems (CPS).Unfortunately, at present there is a wide gap in our understanding that limits this goal: a stark divide exists between algorithms for decision-making, sensing, and motion, and underlying computational resources. This project therefore seeks to define computation-aware autonomy by answering the following questions: (1) How does an environment impact computation? (2) How should autonomy adapt to improve computational awareness? (3) How are computational resources optimized at run-time in support of autonomy? and (4) How is autonomy software rendered resilient to errors? This project aims to answer these questions through optimization, computational resource management, and software resilience, with evaluation in an outdoor robotic testbed. Finally, the broader impacts of this work include: (1) K-12 academic experiences for underrepresented students in collaboration with Virginia Tech's Center for Enhancement of Engineering Diversity; (2) autonomy curriculum and design projects; and (3) participation in a series of symposiums through the Ridge and Valley chapter of the Association for Unmanned Vehicle Systems International.This project focuses on the investigation of: (1) a unified theory and scalable algorithms for multi-agent task allocation and motion planning with constraints on run-time resource optimization and software reliability; (2) efficient analysis and optimization techniques for run-time resource optimization in time-critical CPS; (3) lightweight and flexible methodologies for achieving soft error resilience in computational kernels for autonomy; and (4) a heterogeneous multi-agent testbed for target tracking and infrastructure mapping missions. This project will advance knowledge in the largely unexplored areas of computation and reliability-aware task allocation and motion planning, run-time resource optimization, and flexible software reliability. The new unified approach closes the loop for robust task allocation and motion planning as it acts as a fundamental tool to advance scalability, adaptability, resiliency, safety, security, and usability of CPS with provable behaviors in increasingly complex multi-agent missions across dynamic environments.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.

我们正在进入一个前所未有的信息获取时代，变革性的方法论正在展示自主驱动的未来的清晰愿景。自动驾驶汽车、精准农业、机器人监控和基础设施检查只是正在经历自主革命的几个领域。为了继续朝着这个有希望的方向发展，我们必须促进不同网络物理系统（CPS）的安全可靠的协调。不幸的是，目前我们的理解存在巨大差距，限制了这一目标：之间存在明显的分歧用于决策、传感和运动的算法以及底层计算资源。因此，该项目试图通过回答以下问题来定义计算感知的自治：（1）环境如何影响计算？ （2）自治应如何适应以提高计算意识？ (3) 如何在运行时优化计算资源以支持自治？ (4) 自治软件如何能够抵御错误？该项目旨在通过优化、计算资源管理和软件弹性来回答这些问题，并在室外机器人测试台上进行评估。最后，这项工作的更广泛影响包括：(1) 与弗吉尼亚理工大学工程多样性增强中心合作，为代表性不足的学生提供 K-12 学术体验； （2）自主课程和设计项目； (3) 通过国际无人驾驶车辆系统协会山脊和山谷分会参加一系列研讨会。该项目重点研究：(1) 用于多智能体任务分配和运动的统一理论和可扩展算法在运行时资源优化和软件可靠性约束下进行规划； (2) 时间关键型 CPS 中运行时资源优化的高效分析和优化技术； (3) 在自治计算内核中实现软错误恢复的轻量级且灵活的方法； (4) 用于目标跟踪和基础设施测绘任务的异构多智能体测试台。该项目将推进计算和可靠性感知任务分配和运动规划、运行时资源优化和灵活的软件可靠性等尚未探索的领域的知识。新的统一方法闭合了稳健的任务分配和运动规划的循环，因为它作为基本工具来提高 CPS 的可扩展性、适应性、弹性、安全性和可用性，并在动态环境中日益复杂的多代理任务中提供可证明的行为该奖项反映了 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标准进行评估，被认为值得支持。

项目成果

S-Bottleneck Scheduling with Safety-Performance Trade-offs in Stochastic Conditional DAG Models

随机条件 DAG 模型中具有安全性能权衡的 S 瓶颈调度

• 发表时间: 2021
• 期刊: Industry Challenge
• 作者: Ashrarul Haq Sifat, Xuanliang Deng

A General and Scalable Method for Optimizing Real-Time Systems with Continuous Variables

• DOI: 10.1109/rtas58335.2023.00017
• 发表时间: 2023-05
• 期刊: 2023 IEEE 29th Real-Time and Embedded Technology and Applications Symposium (RTAS)
• 作者: Sen Wang;Ryan K. Williams;Haibo Zeng

Towards computational awareness in autonomous robots: an empirical study of computational kernels

• DOI: 10.1007/s40747-023-01059-7
• 发表时间: 2021-12
• 期刊: Complex & Intelligent Systems
• 影响因子: 5.8
• 作者: Ashrarul H. Sifat;Burhanuddin Bharmal;Haibo Zeng;Jiabin Huang;Changhee Jung;Ryan K. Williams

A Safety-Performance Metric Enabling Computational Awareness in Autonomous Robots

• DOI: 10.1109/lra.2023.3300251
• 发表时间: 2023-09
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Ashrarul H. Sifat;Xuanliang Deng;Burhanuddin Bharmal;Sen Wang;Shao-Yu Huang;Jiabin Huang;Chang-Rae Jung;Haibo Zeng;Ryan K. Williams

A General Scheduling Framework for Multi-objective Real-time Systems

多目标实时系统的通用调度框架

• 发表时间: 2021
• 期刊: Industry Challenge
• 作者: Sen Wang, Ashrarul Haq