Image-based supervision of a periodically working machine
Most industrial robots perform a periodically repeating choreography. Our aim is to detect disturbances of such a periodic process by a visual inspection system that can be trained with a minimum of human effort and interaction. We present a solution that monitors the robot with a time-of-flight 3D...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2013
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| In: |
Pattern analysis and applications
Year: 2011, Jahrgang: 16, Heft: 3, Pages: 407-416 |
| ISSN: | 1433-755X |
| DOI: | 10.1007/s10044-011-0245-7 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1007/s10044-011-0245-7
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| Verfasserangaben: | Mario Frank, Fred A. Hamprecht |
MARC
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| 520 | |a Most industrial robots perform a periodically repeating choreography. Our aim is to detect disturbances of such a periodic process by a visual inspection system that can be trained with a minimum of human effort and interaction. We present a solution that monitors the robot with a time-of-flight 3D camera. Our system can be trained using a few unperturbed cycles of the periodic process. More specifically, principal components are used to find a low-dimensional approximation of each frame, and a One-Class Support Vector Machine is used for one-class learning. We propose a novel scheme for automatic parameter tuning, which exploits the fact that successive images of the training class should be close in feature space. We present exemplary results for a miniature robot setup. The proposed strategy does not require prior information on the dimensions of the machine or its maneuvering range. The entire system is appearance-based and hence does not need access to the robot’s internal coordinates. | ||
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