Using a Flexible Skill-Based Approach to Recognize Objects in Industrial Scenarios

Rasmus Skovgaard Andersen, Casper Schou, Jens Skov Damgaard, Ole Madsen

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

Traditional industrial robots are highly efficient and precise and therefore well suited for carrying out simple, repetitive tasks. They are, however, complicated and time consuming to setup and re-program to perform new tasks. Skill-based programming attempts to reduce both the required time as well as the need for highly specialized staff for setting up modern collaborative robots. This paper proposes a skill for recognition and classification of different objects. The skill is parameterized using manual kinesthetic teaching, and machine learning based on SIFT features, Bag of Words, and SVM is used to classify objects. A user study with 20 test participants shows that robotics novices after only a short introduction are able to instruct the skill and combine it with other skills (pick and place) to program a complete task.
Original languageEnglish
Title of host publicationProceedings of ISR 2016: 47st International Symposium on Robotics
Number of pages8
PublisherVDE Verlag GMBH
Publication dateSep 2016
Pages399-406
ISBN (Print)978-3-8007-4231-8
Publication statusPublished - Sep 2016
EventISR 2016: 47st International Symposium on Robotics - München, Germany
Duration: 21 Jun 201622 Jun 2016
https://conference.vde.com/isr2016/Pages/Start.aspx

Conference

ConferenceISR 2016: 47st International Symposium on Robotics
CountryGermany
CityMünchen
Period21/06/201622/06/2016
Internet address

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  • Cite this

    Andersen, R. S., Schou, C., Damgaard, J. S., & Madsen, O. (2016). Using a Flexible Skill-Based Approach to Recognize Objects in Industrial Scenarios. In Proceedings of ISR 2016: 47st International Symposium on Robotics (pp. 399-406). VDE Verlag GMBH. http://ieeexplore.ieee.org/document/7559145/