SGER: Vision-Based Control of Mechanical Systems via Spatial Sampling Kernels

SGER：通过空间采样内核对机械系统进行基于视觉的控制

• 批准号: 0625708
• 负责人: Noah Cowan
• 金额: $ 6.49万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-15 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0625708&HistoricalAwards=false
• 关键词: SGER; Vision; Based; Control; Mechanical

Vision-based control exemplifies problems in which complex high-dimensional data must be transformed into meaningful action, a fundamental problem in control systems theory. In typical vision-based control systems, information in the visual signal is abstracted and sent to a control algorithm as a geometric measurement. This framework, predicated on the a priori collapse of information-rich visual signals to a few image coordinates, relies on infallible visual tracking and perfect feature correspondence. While conceptually convenient, this approach is fundamentally limited. This project aims to create a unified approach to vision-based control that directly utilizes the underlying visual signals, not simply visual geometry. The approach combines spatial sampling kernels with Lyapunov stability theory. These techniques will facilitate the design of provably stable vision-based control systems for wide classes of visual signals, geometric motions, and system dynamics. How does one convert high-dimensional data into useful action? The answer lies in the ability to distill massive amounts of data for example, a video stream into to low-dimensional, task-specific information for example, robotic movements in real time. This project seeks to develop a scientific basis for the efficient reduction of complex data streams into effective action. In particular, this project uses vision-based control to investigate new mathematical methods for collapsing complex signals in a task-specific way that admits clear analysis and provable performance. Central to the research is the idea that the connection between information processing and control need-not be ad hoc; instead, mathematical theorems that link information to action can provide performance guarantees under real-world circumstances.

基于视觉的控制解决了复杂的高维数据必须转化为有意义的行为的问题，这是控制系统理论中的一个基本问题。在典型的基于视觉的控制系统中，视觉信号中的信息被提取并作为几何测量发送到控制算法。这个框架，基于先验的信息丰富的视觉信号的几个图像坐标的崩溃，依赖于可靠的视觉跟踪和完美的特征对应。虽然概念上方便，但这种方法从根本上是有限的。该项目旨在创建一种统一的基于视觉的控制方法，直接利用底层视觉信号，而不仅仅是视觉几何。该方法结合了空间采样核和李雅普诺夫稳定性理论。这些技术将促进可证明稳定的视觉为基础的控制系统的设计，广泛的视觉信号，几何运动和系统动力学。如何将高维数据转换为有用的操作？答案在于将大量数据（例如，视频流）提取为低维特定任务信息（例如，真实的机器人运动）的能力。该项目旨在为有效减少复杂的数据流并将其转化为有效行动奠定科学基础。特别是，该项目使用基于视觉的控制来研究新的数学方法，以特定于任务的方式来折叠复杂信号，从而实现清晰的分析和可证明的性能。这项研究的核心思想是，信息处理和控制之间的联系不需要是特设的;相反，将信息与行动联系起来的数学定理可以在现实世界的情况下提供性能保证。