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

无人机系统（UAS）或无人机在数据收集、监控和与环境交互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、感知和控制能力，以及适应不断变化的环境的灵活性。然而，与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，例如从航点导航过渡到环境样本收集时。一个计算框架，利用释放的资源将开发允许自主代理集中注意力在需要的地方，例如，远离导航和感知。总之，这些将提供一个蓝图，利用类似的算法与可调性能（例如，随时算法），这些算法可以适用于其他机器人平台以及水上、太空或地面车辆。这些技术创新将提高智能体的能力，使其能够更多地学习，更准确地感知，收集更好的数据，并更恰当地对不断变化的环境和使命目标做出反应。具体到无人机系统，该项目将通过敏捷规划、有针对性和持续的情报、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。在实现项目目标的同时，还开展了外联和教育活动，重点是提高农村人口对投资于科学和技术研究的价值的认识。教育工作，针对K-12，本科生，研究生和成人参与的目的是显着增加CPS教育管道在中西部。该项目的重点是实现其目标，通过提供一个完整的框架，一类性能可调，资源感知算法称为“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），将为系统的最优控制，控制合成和性能分析提供数学基础，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，使用的CHS形式主义，计算工作量的预测形成一种新的协同调节实时内核（CRTK）的基础上，动态地重新分配计算资源，同时保证实时调度的可行性。最后，一个共同调节马尔可夫决策过程（MDP）形成了一个资源感知的自适应无人机自动驾驶仪的规划部分。该系统将在一个多智能体，雨林监测方案，需要监测期间，植物采样，并emplacement的sensor.This奖项反映了NSF的法定使命，并已被认为是值得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的支持。

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.