CAREER: Foundations for a Resource-Aware, Cyber-Physical Vehicle Autonomy

职业：资源感知、网络物理车辆自主的基础

基本信息

批准号：
2047971
负责人：
Justin Bradley
金额：
$ 50万
依托单位：
University of Nebraska-Lincoln
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-01 至 2026-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047971&HistoricalAwards=false
关键词：
CAREER Foundations Resource Aware Cyber

项目摘要

Unmanned Aircraft Systems (UASs), or drones, have tremendous scientific, military, and civilian potential for data collection, monitoring, and interacting with the environment. These activities require high levels of reasoning, perception, and control, and the flexibility to adapt to changing environments. However, like other automated agents, UAS don't possess the ability to refocus their attention or reallocate resources to adapt to new scenarios and adjust performance. This project will provide a new class of control and planning algorithms capable of adjusting performance as computing resources are continually reallocated, such as when transitioning from waypoint navigation to environmental sample collection. A computing framework to make use of freed resources will be developed allowing autonomous agents to focus attention where it is needed, for example, away from navigation and to perception. Together, these will provide a blueprint for making use of similar algorithms with adjustable performance (e.g., anytime algorithms) which can be adapted to other robotics platforms, as well as water, space, or ground vehicles.These technology innovations will improve the ability of agents to learn more, perceive more accurately, collect better data, and respond more appropriately to changing environments and mission objectives. Specific to UAS, this project will help maintain U.S. air superiority goals through agile planning, targeted and persistent Intelligence, Surveillance, and Reconnaissance (ISR), and flexibility and adaptability. The project goals are coupled with outreach and educational activities focused on increasing the understanding of rural populations of the value of investing in scientific and technological research. The educational efforts, targeted at K-12, undergraduate, graduate, and adult engagement are designed to dramatically increase the CPS educational pipeline in the Midwest.The project focuses on achieving its goals by providing a complete framework for a class of performance-adjustable, resource-aware algorithms called "co-regulation." First, a new modeling and analysis framework, Co-regulated Hybrid Systems (CHS), will provide a mathematical foundation for optimal control, control synthesis, and performance analysis for systems that can dynamically vary sampling rate and other computational resources to adjust performance. Next, using the CHS formalism, computational workload is predicted forming the basis for a novel Co-regulated Real-Time Kernel (CRTK) to dynamically reallocate computing resources while guaranteeing real-time schedule feasibility. Finally, a co-regulated Markov Decision Process (MDP) forms the planning portion of a resource-aware autopilot for adaptable UAS. The system will be implemented in a multi-agent, rainforest monitoring scenario requiring periods of surveillance, sampling of plants, and emplacement of sensors.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.

无人飞机系统（UASS）或无人机具有巨大的科学，军事和平民潜力，以收集数据，监测和与环境互动。这些活动需要高水平的推理，感知和控制，以及适应不断变化的环境的灵活性。但是，像其他自动化代理一样，UAS没有能力重新集中注意力或重新分配资源以适应新场景并调整性能。该项目将提供一种新的控制和计划算法，能够随着计算资源的持续重新分配，例如从Waypoint导航到环境样本收集。将开发一个计算框架来利用释放资源，使自主代理可以在需要的地方集中注意力，例如，远离导航和感知。共同，这些将提供一个可调节性能（例如，任何时间算法）的类似算法的蓝图，可以适应其他机器人平台，以及水，空间或地面车辆。这些技术创新将提高代理商的能力，以提高代理商的能力，以更高的了解，以更准确地说服更准确地说，并响应更好的数据，并获得了更改，并获得了更改，并得到了更改，并适当地进行了宣称，并适当地进行了宣传。该项目特定于UAS，通过敏捷的计划，有针对性和持久的情报，监视和侦察（ISR）以及灵活性和适应性来帮助维持美国的空中优势目标。项目目标与外展和教育活动相结合，重点是增加对科学和技术研究投资价值的农村人群的理解。针对K-12，本科生，研究生和成人参与的教育工作旨在显着增加中西部的CPS教育管道。该项目着重于通过为一类可进行性能的，可供资源认识的算法提供完整的框架来实现其目标，称为“共同进行”。首先，一个新的建模和分析框架，共同调节的混合系统（CHS）将为系统提供最佳控制，控制合成和性能分析的数学基础，这些系统可以动态地改变采样率和其他计算资源来调整性能。接下来，使用CHS形式主义，预测计算工作负载将形成新型共同调节的实时内核（CRTK）的基础，以动态重新分配计算资源，同时保证实时时间表可行性。最后，共同调节的马尔可夫决策过程（MDP）构成了适应性UAS的资源感知自动驾驶仪的计划部分。该系统将在需要监视的时期，植物抽样以及对传感器的安置时实施，该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。