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

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

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Abstrakt

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.
OriginalsprogEngelsk
TitelProceedings of ISR 2016: 47st International Symposium on Robotics
Antal sider8
ForlagVDE Verlag GMBH
Publikationsdatosep. 2016
Sider399-406
ISBN (Trykt)978-3-8007-4231-8
StatusUdgivet - sep. 2016
BegivenhedISR 2016: 47st International Symposium on Robotics - München, Tyskland
Varighed: 21 jun. 201622 jun. 2016
https://conference.vde.com/isr2016/Pages/Start.aspx

Konference

KonferenceISR 2016: 47st International Symposium on Robotics
LandTyskland
ByMünchen
Periode21/06/201622/06/2016
Internetadresse

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Citationsformater

Andersen, R. S., Schou, C., Damgaard, J. S., & Madsen, O. (2016). Using a Flexible Skill-Based Approach to Recognize Objects in Industrial Scenarios. I Proceedings of ISR 2016: 47st International Symposium on Robotics (s. 399-406). VDE Verlag GMBH.