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Active Filters

Active Filters

Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:

Filtration

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Filtration is a physical separation process that involves passing a suspension through a porous medium to separate solids from fluids. During filtration, solids collect on the porous medium while liquids, also collectively known as the filtrate, pass through. The filtration medium is selected based on the filtration purpose, quantity, and nature of the precipitate. The general criteria for a suitable filtering medium are that it is inert, mechanically strong, nonabsorbent toward dissolved...

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Related Experiment Video

Updated: May 23, 2026

Single Molecule Fluorescence Microscopy on Planar Supported Bilayers

Single Molecule Fluorescence Microscopy on Planar Supported Bilayers

Published on: October 31, 2015

Matched filters for bin picking.

J D Dessimoz¹, J R Birk, R B Kelley

¹Department of Electrical Engineering, University of Rhode Island, Kingston, RI.; Ecole d'Ingenieurs de l'Etat de Vaud, Yverdon-les-Bains, Switzerland.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|April 14, 2012

Summary

This summary is machine-generated.

This study introduces matched filters for robot bin picking, enabling robots to grasp randomly placed workpieces. This vision-guided approach significantly improves success rates in automated assembly and material handling tasks.

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Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Robots require precisely positioned workpieces for assembly and material handling.
Bin picking techniques aim to reduce the need for precise workpiece presentation.
Existing heuristic methods for bin picking have limitations.

Purpose of the Study:

To apply matched filters for robot vision systems to acquire randomly stored workpieces from bins.
To redefine matched filters considering robot end-effector geometry and mechanics for grasping.
To improve the efficiency and success rate of automated bin picking operations.

Main Methods:

Application of matched filters tailored to robot end-effector grasping points.
Development of filters matching local workpiece structures for successful grasping.
Utilizing statistical and Fourier models to determine filter requirements.
Experimental validation on a system with a camera, Cartesian robot, and parallel-jaw hand.

Main Results:

Matched filters effectively identify graspable workpiece structures.
A small number of filters (typically four) are sufficient for high orientation accuracy (two degrees).
Experimental success rates of approximately 90% within three attempts were achieved.
Local feature matching significantly reduces computational costs.

Conclusions:

Matched filters offer a robust solution for robot bin picking.
The proposed method enhances the capability of robots in handling randomly oriented objects.
This approach advances automated assembly and material handling by reducing workpiece presentation constraints.