Hierarchical Robot Multi-Sensor Data Fusion System

这项研究的目的是研究如何实际融合多传感器数据来表示机器人任务环境的基本问题。首席研究人员将首先开发一种基于四个不同阶段的分级策略来获取机器人的感觉信息：“远”、“近”、“触”和“操纵”。由于每个阶段由公共信息和阶段指定信息组成，首席调查员将每个阶段的信息打包成面向阶段的不同模板。下一步将是开发传感器模型、传感器协调和多个传感器数据的融合。在传感器融合过程开始之前，将使用一种新的“置信度距离度量”和“距离矩阵”作为检测传感器误差的标准。将创建一个数学模型来表示确定最佳融合传感器数据的置信度度量的水平。随后，首席调查员将对四个阶段的所有信息进行分组，以建立对象信息模板并完成任务环境的表示。这种方法允许以低级别的方式(以最小化噪声数据的影响)和高级别的方式(对传感器之间的影响和数据组合的方式进行约束)来合并数据。这项工作可能会澄清一些问题：有故障的传感器隔离的有用性，组合数据源的准确性，以及用于阶段性传感的各种模板的总体凝聚力。

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.