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

无人驾驶飞机系统(UAS)或无人机在数据收集、监测和与环境互动方面具有巨大的科学、军事和民用潜力。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。然而，像其他自动化代理一样，UAS不具备重新集中注意力或重新分配资源以适应新场景和调整性能的能力。该项目将提供一类新的控制和规划算法，能够随着计算资源的不断重新分配而调整性能，例如当从路点导航过渡到环境样本采集时。将开发一个利用释放的资源的计算框架，允许自主代理将注意力集中在需要的地方，例如，远离导航和感知。总而言之，这些技术创新将为使用性能可调的类似算法(例如，随时算法)提供蓝图，这些算法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学习更多、更准确感知、收集更好数据、更适当地响应不断变化的环境和任务目标的能力。具体到无人机，该项目将通过灵活的规划、有针对性和持续性的情报、监视和侦察(ISR)以及灵活性和适应性来帮助保持美国的空中优势目标。项目目标与外联和教育活动相结合，侧重于提高农村人口对投资于科学和技术研究的价值的认识。这些针对K-12、本科生、研究生和成人参与度的教育努力旨在极大地增加中西部地区的CPS教育渠道。该项目专注于通过为一类性能可调、资源感知的算法提供一个完整的框架来实现其目标，该算法称为“共同调节”。首先，一个新的建模和分析框架--协同调节混合系统(CHS)--将为动态改变采样率和其他计算资源以调整性能的系统的最优控制、控制综合和性能分析提供数学基础。然后，使用CHS形式对计算工作量进行预测，为新的协同调节实时内核(CRTK)在保证实时调度可行性的同时动态地重新分配计算资源奠定了基础。最后，共同调节的马尔可夫决策过程(MDP)形成了用于适应性UAS的资源感知自动驾驶仪的规划部分。该系统将在多代理雨林监测方案中实施，需要一段时间的监测、植物采样和传感器部署。该奖项反映了NSF的法定使命，并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，被认为值得支持。

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.