gain-compensation-methodology-for-sinusoidal-scan-galvanometer-mirror

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Galvanometer mirrors are used for optical applications such as target tracking, drawing, and scanning control because of their high speed and accuracy. ...

University of Tokyo

In this paper, a new measurement method of the rotation angle of a rotor, which is named the 'visual encoder,' was proposed. This method is based on the principles of the vision-based method and the optical encoder, and realized by using a high-speed vision system. The visual encoder shows advantageous features such as non-contact, high-resolution and robustness against the free motion and the fluctuation of the rotation axis. A high resolution method to increase the measurement resolution was also suggested. The accuracy and the robustness of the visual encoder were confirmed through the experimental verifications, and the operation was possible at 6,000 rpm even under the fluctuation of rotation axis.

Visual encoder: robust and precise measurement method of rotation angle via high-speed RGB vision.

We propose a method to achieve precise sine-wave path tracking for real-time motion-blur compensation to extend the corresponding frequency spectrum in proportional-integral-differential (PID) control by using a pre-emphasis technique. We calculate pre-emphasis coefficients in advance to follow a sine wave with a gain of 0 dB and multiply the input signal by these pre-emphasis coefficients. In experiments, we were thus able to extend the greatest frequency from 100 to 500 Hz and achieve gain improvement of approximately 3 dB at 400 and 500 Hz. For the application of inspection, we confirmed that motion blur is significantly reduced when the system operates at high frequency, and we achieved a responsiveness 3.3 times higher than that of our previous system.

Gain-compensated sinusoidal scanning of a galvanometer mirror in proportional-integral-differential control using the pre-emphasis technique for motion-blur compensation.

It is traditionally difficult to implement fast and accurate position regulation on an industrial robot in the presence of uncertainties. The uncertain factors can be attributed either to the industrial robot itself (e.g., a mismatch of dynamics, mechanical defects such as backlash, etc.) or to the external environment (e.g., calibration errors, misalignment or perturbations of a workpiece, etc.). This paper proposes a systematic approach to implement high-performance position regulation under uncertainties on a general industrial robot (referred to as the main robot) with minimal or no manual teaching. The method is based on a coarse-to-fine strategy that involves configuring an add-on module for the main robot's end effector. The add-on module consists of a 1000 Hz vision sensor and a high-speed actuator to compensate for accumulated uncertainties. The main robot only focuses on fast and coarse motion, with its trajectories automatically planned by image information from a static low-cost camera. Fast and accurate peg-and-hole alignment in one dimension was implemented as an application scenario by using a commercial parallel-link robot and an add-on compensation module with one degree of freedom (DoF). Experimental results yielded an almost 100% success rate for fast peg-in-hole manipulation (with regulation accuracy at about 0.1 mm) when the workpiece was randomly placed.

Article	Year
Visual encoder: robust and precise measurement method of rotation angle via high-speed RGB vision. Optics express\| PubMed ID: 27410355	2016
Gain-compensated sinusoidal scanning of a galvanometer mirror in proportional-integral-differential control using the pre-emphasis technique for motion-blur compensation. Applied optics\| PubMed ID: 27463919	2016
Applying High-Speed Vision Sensing to an Industrial Robot for High-Performance Position Regulation under Uncertainties. Sensors (Basel, Switzerland)\| PubMed ID: 27483274	2016

Taku Senoo

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