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

有意义：自主系统的同步传感器配置和最优控制

基本信息

批准号：
2126818
负责人：
Raghvendra Cowlagi
金额：
$ 53万
依托单位：
Worcester Polytechnic Institute
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-11-01 至 2024-10-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126818&HistoricalAwards=false
关键词：
Making Sense Simultaneous Sensor Configuration

项目摘要

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.

该项目将为未来大规模应用中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反应。例如，在许多道路被洪水淹没的灾后情况下，确定可行的疏散路线将人们运送到更安全的地点非常重要。我们希望尽可能地自动化这个过程：合并来自各种来源的信息，并快速规划安全路线。无人驾驶飞行器网络可以直观地调查洪水的范围，并确定可通行的道路。有关道路状况、停电线路和其他环境因素的信息也可以从各种来源获得，例如交通摄像头或社交媒体帖子。目前的自治科学是不够的，因为信息收集和规划通常被视为两个独立的问题。相比之下，本项目强调同时进行信息收集和规划，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。利用这种方法，可以比利用当前技术更快地实现最优计划。因此，这项研究的结果可能使人们能够对不良事件做出更快的反应，这反过来可能有助于减少生命损失以及对财产和环境的损害。在新的自主系统中，传感器可以是外感受性的、多模式的和可配置的，例如，可以调整诸如位置和摇摄-倾斜-缩放的参数。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，从而闭合估计和控制之间的回路。这与控制设计中传统使用的分离原则不同。通过这种集成方法，可以预期，与传统范例相比，控制目标可以以更小的风险和显著更低的传感器数据量来实现，在某些情况下，测量数量级更少。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，通过最大化上下文相关信息增益度量来实现给定控制设计的最优传感器配置，然后根据由更新的传感器配置生成的数据来确定最优控制设计。该奖项反映了NSF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。