A magnetic and inertial measurement unit (MIMU) usually measures acceleration, rotation rate, and earth's magnetic field in order to determine a body's attitude. In order to find the orientation information using all sensors a fusion algorithm is used. This paper compares two approaches used for a Attitude and Heading Reference System (AHRS), namely Madgwick and Mahony with a basic fusion approach. Foot mounted MIMU data is used to estimate the Euler angles as well as the position. The results show that Madgwick obtains better heading orientation than Mahony and the basic AHRS approach in terms of the error (RMSE) of the Euler angles when compared to the ground truth. However, the execution time of Mahony is less than Madgwick with the basic AHRS taking the longest.
We present the definition and development of a three dimensional high precision measurement system for localisation and control of machine tool's drill based on a Michelson-Morley interferometric configuration and a laser tracking subsystem to redirect the measurement beam onto a retro-reflector positioned in the tool nearness. The system measures the retro-reflector position in real time with 5 microns accuracy up to 2 m/s processing material velocities and it is used to carry out an on-line control feedback of the drill position in the machine tool allowing a drill tool replacement during the fabrication process. The system has been integrated and tested in an especially designed machine tool built for this application.
We present a computer vision system for measuring the weight of gobs during a glass-forming process, and a control strategy to automatically correct any weight deviation from a given set point. The system is based on a reliable gob area estimation using image-processing algorithms. A monochrome CCD high-resolution camera and a photodetector for synchronizing acquisition are used for registering gob images. Assuming that the gob has symmetry of revolution about the vertical axis, the proposed system estimates the weight of gobs with accuracy better than ±0.75%. A learning weight control strategy is proposed based on a proportional-integral (PI)-repetitive control scheme. The weight deviation from a set point is used as a control signal to adjust the glass flow into the feeder. This regulation scheme enables effective weight control, canceling mid- and long-term effects. The tracking error of ±1.5% means a reduction of 40% when compared with a traditional PI controller.
This paper presents a computer vision system for measuring the weight of gobs during a glass forming process, and a control strategy to correct automatically any weight deviation from a given set-point.
During the formation of molten glass gobs, several noise sources can cause a deviation in the weight from a predefined reference value. Among them, there is a random white-noise disturbance caused by the lack of synchronisation of mechanical devices, the periodic disturbances due to changes in the spinning direction of the tube inside the feeder, and some long-term drifts caused by variations in temperature and viscosity of the raw glass material. The gob weight measurement system developed is based on a monochrome CCD high-resolution camera and photo-detector for synchronizing the frame acquisition. The molten glass provides the illumination, so a high contrast image is obtained with a bright object and dark background. Several image-processing algorithms are presented for reliable area estimation. Assuming that the gob is a symmetric geometry of revolution and uniform mass density, the proposed system estimates the weight of gobs with an accuracy better than ±0.75%. A learning weight control strategy is proposed based on a PI-repetitive control scheme. The weight deviation from a set point is used as a control signal to adjust the glass flow into the feeder. This regulation scheme allows effective weight control, canceling mid and long-term effects. The tracking error, ±1.5%, means a reduction of 40% when compared with a traditional PI controller.