Collaborative Research: ITR: A Robotics-Based Computational Environment to Simulate the Human Hand

合作研究：ITR：基于机器人的模拟人手的计算环境

• 批准号: 0312693
• 负责人: Peter Allen
• 金额: $ 25万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-07-01 至 2006-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0312693&HistoricalAwards=false
• 关键词: Collaborative; Research; ITR; Robotics; Based

This project develops an integrated Bayesian framework for vision based control of Unmanned Aerial Vehicles (UAVs) through fundamental research in 1) model-based nonlinear state and parameter estimation, 2) intelligent adaptive control, and 3) image processing. We specifically address how real-time video data can be processed with ground-based sensors (and on-board avionics) to extract spatial and situational information (e.g., vehicle state and model parameters). Using only stationary video cameras, information from the sequence of images are integrated with an adaptive controller that transmits actuator commands directly to the UAV. Our research infrastructure consist of an X-Cell-60 R/C helicopter with custom avionics, video cameras on the ground, and a PC ground-station to perform all necessary processingA key aspect is to go beyond traditional vision based motion estimation and tracking, utilizing new approaches to recursive Bayesian estimation allowing full coupling with the control system. Heuristically, this involves the propagation of probabilistic density estimates for the state (vehicle position, attitude, and velocities) and model parameters (mass, moments of inertia, aerodynamic forces, etc.). The vision components models the ``image likelihood'' and describes the probability of observing the image given the current state. The estimation combines the vision measurements with the dynamic vehicle model in a recursive filtering procedure using a Sigma-Point Filter (SPF) framework. SPF methods are a recent development in machine learning, and are shown to be far superior to standard EKF based estimation approaches. The intellectual merit of the research contributes to both the individual component areas as well as the integrated whole. The integration of the different components in the proposed manner represents an interdisciplinary new approach, providing new research opportunities and applications in integrated sensing, information processing, and control. Beyond basic research, the broader impact to technology includes the obvious commercial and military applications that can be studied in this controlled environment (e.g. visually assisted vertical take-off and landing for ship board helicopters, or agile maneuvering through urban environments). The core technologies can also be extended to other information technology areas from image tracking and detection, to control of complex biological systems.

本项目通过1）基于模型的非线性状态和参数估计，2）智能自适应控制和3）图像处理的基础研究，开发了一个集成的贝叶斯框架，用于无人机（UAV）的基于视觉的控制。我们具体讨论了如何用地面传感器（和机载航空电子设备）处理实时视频数据，以提取空间和态势信息（例如，车辆状态和模型参数）。 仅使用固定摄像机，来自图像序列的信息与自适应控制器集成，该自适应控制器将致动器命令直接发送到UAV。我们的研究基础设施包括一个X-Cell-60 R/C直升机与定制的航空电子设备，视频摄像机在地面上，和一个PC地面站，以执行所有必要的处理一个关键的方面是超越传统的基于视觉的运动估计和跟踪，利用新的方法来递归贝叶斯估计允许与控制系统的充分耦合。启发式地，这涉及状态（飞行器位置、姿态和速度）和模型参数（质量、惯性矩、气动力等）的概率密度估计的传播。 视觉组件对“图像可能性”进行建模，并描述在给定当前状态下观察图像的概率。该估计将视觉测量与动态车辆模型结合在使用Sigma-Point Filter（SPF）框架的递归滤波过程中。SPF方法是机器学习的最新发展，并且被证明远远上级基于标准EKF的估计方法。研究的知识价值既有助于个别组成领域，也有助于整体。以所提出的方式集成不同的组件代表了一种跨学科的新方法，在集成传感，信息处理和控制方面提供了新的研究机会和应用。除了基础研究之外，对技术的更广泛影响还包括可以在这种受控环境中研究的明显的商业和军事应用（例如，舰载直升机的视觉辅助垂直起飞和降落，或在城市环境中的敏捷机动）。 核心技术还可以扩展到其他信息技术领域，从图像跟踪和检测到控制复杂的生物系统。