Hierarchical Robot Multi-Sensor Data Fusion System

分层机器人多传感器数据融合系统

• 批准号: 8716126
• 负责人: Ren Luo
• 金额: $ 23.16万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-03-15 至 1991-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8716126&HistoricalAwards=false
• 关键词: Hierarchical; Robot; Multi; Sensor; Data

The objective of this research is to investigate the fundamental issues of how to actually fuse multi-sensor data to represent the robot task environment. The principal investigator will first develop a hierarchical strategy for acquiring robot sensory information based on four distinct phases: "Far Away," "Close To," "Touching," and "Manipulation." Because each phase consists of common information and phase-specified information, the principal investigator will package the information for each phase into a phase-oriented distinct template. The next step will be the development of sensor models, sensor coordination, and fusion of multiple sensor data. A new "confidence distance measure" and "distance matrix" will be used as criteria for the detection of sensor errors before the beginning of the sensor fusion process. A mathematical model will be created to represent the level of confidence measures for determining the optimal fused sensor data. Following this, the principal investigator will group all information from the four phases to establish object information templates and complete the representation of the task environment. This approach permits data to be merged in both a low-level way (to minimize the influence of noisy data) and a high level way (constraints are put on the influence between sensors and the way the data is combined). This work can potentially clear the air on a number of issues: the usefulness of faulty sensor isolation, the accuracy of combined data sources, and the overall cohesiveness of the various templates for phased sensing.

本研究的目的是调查 如何真正融合多传感器数据的基本问题， 表示机器人任务环境。 主要研究者 我将首先制定一个分层策略， 感官信息基于四个不同的阶段："遥远"， "接近""触摸"和"操纵"。"因为每个阶段 由公共信息和阶段指定信息组成， 主要研究者将对每个阶段的信息进行打包 转化成一个相位导向的模板。 下一步将是 传感器模型的开发，传感器协调， 多个传感器数据。一种新的"置信距离度量"， "距离矩阵"将被用作检测 传感器融合过程开始之前的传感器错误。 一 将建立数学模型来表示 用于确定最佳融合传感器数据的置信度度量。 在此之后，主要研究者将所有 四个阶段的信息，以建立对象信息 模板并完成任务环境的表示。 这种方法允许在低级别和高级别两种模式中合并数据。 方法（以最大限度地减少噪声数据的影响）和高水平的方法 （对传感器之间的影响和 数据被合并）。 这项工作可能会清除空气中的一个 问题的数量：故障传感器隔离的有用性， 组合数据源的准确性，以及 用于相位感应的各种模板。