Object tracking in robotic micromanipulation by supervised ensemble learning classifier

Zoran Cenev, Janne Venäläinen, Veikko Sariola, Quan Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)

Abstract

Autonomous micromanipulation has a great potential to impact every research field concerning objects at a small scale. In this paper, we report our work on detection and tracking of a transparent SU-8 microchip in 3D Cartesian space during micromanipulation. Conditions such as occlusion, variant object orientation and poor edge prominence hinder the implementation of conventional vision algorithms. To enable tracking in such difficult conditions, an object detection classifier that utilizes an ensemble machine learning algorithm has been implemented. The classifier has been trained with 165 and 85 unique positive and negative samples, respectively. Object detection was achieved at a distance of three times the nominal depth of field with maximum tracking error of only 12 % of the object size.

Original languageEnglish
Title of host publicationProceedings of the 2016 International Conference on Manipulation, Automation and Robotics at Small Scales, MARSS 2016
PublisherIEEE
ISBN (Electronic)9781509015108
DOIs
Publication statusPublished - 6 Sep 2016
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Manipulation, Automation and Robotics at Small Scales - Paris, France
Duration: 18 Jul 201622 Jul 2016
Conference number: 1

Conference

ConferenceInternational Conference on Manipulation, Automation and Robotics at Small Scales
Abbreviated titleMARSS 2016
CountryFrance
CityParis
Period18/07/201622/07/2016

Keywords

  • classification
  • detection
  • Ensemble
  • learning
  • machine
  • micro-object
  • micromanipulation
  • robotic

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