Sensors: Intelligent Multi-Sensor Modeling, Identification, and Data Fusion for Automated Manufacturing

自动化制造的智能多传感器建模、识别和数据融合（NSF 提案号 0427597）摘要 这项研究工作的主要目标是开发创新方法，用于在灵活的制造工作单元环境中对来自多个异构信息源/传感器的数据进行建模、识别和融合。这项研究的主要贡献包括将遗传神经模糊方法和贝叶斯估计等人工智能技术与扩展卡尔曼滤波等信号处理和统计技术以及最大似然和期望最大化等参数估计方法相结合。该研究项目将重点关注以下三个主要组成部分：1）精确的传感器建模，包括获取传感器和相关噪声的分析和概率模型，了解其功能和局限性，2）从多个传感器提取实时数据并在通用处理平台上解释数据，3）制定策略以结合来自这些传感器的无噪声数据，以消除不确定性并获得制造系统运行环境的准确模型。所开发的创新理论将通过在杜克大学灵活的实验中心进行的实验进行测试和验证。制造工作单元。由于该研究将强调在制造过程中使用各种传感器，因此制造业将是主要受益者。

Intelligent Multi-Sensor Modeling, Identification, and Data Fusion for Automated Manufacturing(NSF Proposal No. 0427597)AbstractThe primary goal of this research effort is to develop innovative methodologies for modeling, identifying and fusing data from multiple heterogeneous information sources/sensors in a flexible manufacturing workcell environment. Major contributions resulting from this research would consist of integrating artificial intelligence techniques, such as Genetic-Neuro-Fuzzy method and Bayesian estimation, with elements of signal processing and statistical techniques such as Extended Kalman filtering, and parametric estimation methods such as Maximum Likelihood, and Expectation Maximization. This research project would focus on the following three major components: 1) precise sensor modeling which would include obtaining analytical and probabilistic models of sensors and associated noises, understanding their capabilities and limitations, 2) extracting real-time data from multiple sensors and interpreting the data on a common processing platform, and 3) developing strategies to combine the noise-free data from these sensors to remove uncertainty and to obtain accurate model of the environment the manufacturing system operates in. The innovative theories developed would be tested and validated via experiments conducted at Duke University's flexible manufacturing workcell. Since the research would emphasize the use of a variety of sensors in manufacturing processes, the manufacturing industry would be a major beneficiary.