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

Duke University, Durham NC

Investigators

Abstract

Intelligent Multi-Sensor Modeling, Identification, and Data Fusion for Automated Manufacturing (NSF Proposal No. 0427597) Abstract The 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.