Sensor integration system utilizing fuzzy inference with LED displacement sensor and vision system

Abstract

This paper deals with a 3-D measurement system applied to a curved metal surface carving system, and a multi-sensor integration system based on fuzzy inference. The measurement system consists of two different sensors, a LED displacement sensor and a vision system. The LED displacement sensor is used as a part of the vision system based on the active stereo sensing method. In addition, the LED displacement sensor's outputs are used for calibrating camera parameters. Therefore, the system can calibrate the camera parameters easily. Neural networks are used to compensate the output of the image processing for some errors, such as camera parameter's error and lens distortion. By utilizing the neural network, we can use a vision system as accurately as possible. We use a sensor integration method based on the fuzzy inference. Fuzzy inference's input consists of information on the change in the sensor output and the position change of the sensor system, together with the environmental data of measurement. For this integration system, we can use the sensory system accurately. The proposed system is shown to be effective through extensive experiments.

Design and development of fuzzy expert system for Handy Board

“…The implemented fuzzy logic controller showed more constant steel thickness compared to the manually operated process. [27].Mitsubishi Heavy Industries of Japan [28] has been using fuzzy logic to control the weld-line tracking by a robot. The sensor signals here are the horizontal and vertical displacements of the welding point.…”