Making Sense: Simultaneous Sensor Configuration and Optimal Control for Autonomous Systems

该项目将为未来大规模应用的自主系统提供新的科学知识，包括对自然灾害或工业事故等不利事件的应急响应。例如，在灾后许多道路被洪水淹没的情况下，确定可行的疏散路线将人们运送到更安全的地方是很重要的。尽可能自动化这个过程是可取的：合并来自不同来源的信息并快速规划安全路线。无人机网络可以直观地调查洪水的程度，并识别可通行的道路。有关道路状况、倒下的电线和其他环境因素的信息也可以从交通摄像头或社交媒体帖子等不同来源获得。由于信息收集和计划通常被视为两个独立的问题，目前的自主科学是不充分的。相比之下，本项目强调同时收集信息和规划，即在具体规划目标范围内确定和部署最相关信息来源的方法。采用这种方法，可以比现有技术更快地实现最佳计划。因此，这项研究的结果可能使对不良事件的反应更快，这反过来可能有助于减少生命损失和财产和环境损害。在新的自主系统中，传感器可能是外感的、多模态的和可配置的，例如，位置和平移-倾斜变焦等参数可能是可调的。本研究旨在通过配置传感器来收集与最优控制目标最相关的数据，从而关闭估计和控制之间的回路。这是对传统控制设计中使用的分离原则的背离。有了这种集成方法，预计控制目标可以以更低的风险和更少的传感器数据量实现，在某些情况下，与传统范例相比，测量量减少了几个数量级。该研究将新的机器学习工具与控制和估计技术相结合，以执行迭代传感和控制算法。在每次迭代中，通过最大化与上下文相关的信息增益度量来实现给定控制设计的最优传感器配置，然后根据更新的传感器配置生成的数据确定最优控制设计。这些迭代的不动点是耦合问题的近最优解。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project will contribute new scientific knowledge for future autonomous systems in large-scale applications, including emergency response to adverse events such as natural disasters or industrial accidents. As an example, in a post-disaster scenario where many roads are flooded, it is important to determine viable evacuation routes to transport people to safer locations. It is desirable to automate this process as much as possible: merge information from various sources and quickly plan the safe route. A network of unmanned aerial vehicles can visually survey the extent of flooding and identify passable roads. Information about road conditions, downed power lines, and other environmental factors may also be gained from diverse sources such as traffic cameras or social media posts. The current science of autonomy is inadequate because information-gathering and planning are typically treated as two separate problems. By contrast, this project emphasizes simultaneous information-gathering and planning, namely, methods to identify and deploy the most relevant sources of information in the context of a specific planning objective. With this approach, optimal plans may be achieved significantly faster than is possible with current technology. Consequently, the outcomes of this research may enable faster responses to adverse events, which in turn may help reduce loss of life and damage to property and the environment.In new autonomous systems, sensors may be exteroceptive, multimodal, and configurable, e.g., parameters such as location and pan-tilt-zoom may be tuned. This research aims to close the loop between estimation and control by configuring sensors to collect data most relevant to an optimal control objective. This is a departure from the separation principle traditionally used in control design. With such an integrated approach, it is anticipated that the control objective can be achieved with less risk and significantly lower volumes of sensor data, in some cases orders of magnitude fewer measurements, compared to the traditional paradigm. The research combines new machine learning tools with control and estimation techniques to execute an iterative sensing and control algorithm. At each iteration, an optimal sensor configuration is achieved for a given control design by maximizing a context-relevant information gain metric, and an optimal control design is then determined from the data generated by the updated sensor configuration. The fixed point of these iterations is a near-optimal solution to the coupled problem.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.