Part Shape Information Acquisition Based on Stereoscopic Vision

Abstract:

A clamping robot serving for several NC machines can recognize the best fit in manny blank parts. Forthermore, the shape information acquisition and pose measurment can be achieved by its computer vision system. The key question is that the clamping robot automatively choices the best fit part by statistical result of geometric dimension of all parts. The best fit part is the one that its length and its diameter are mostly proximate to the processing requirement. This choice method can save staff, reduce working allowance, and lessen finished cost. According to the shape information and position information, the robot can confirm the grab order, grab angle and finger joint distance. Firstly, the part image is smoothing by noise reduction technology, secondly, the feature points are extracted. The feature points present the information of the location of the mass center, the dip angle and diameter. Finally, the clamping robot can grab the most fit bar part by means of rotational motion, rectilinear motion, and finger open-close movement when the shape information and location information of the bar part have been confirmed.

• Periodical: Applied Mechanics and Materials (Volume 442)
• Pages: 472-475
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.442.472
• Online since: October 2013
• Authors: Ji Nan Gu, Si Jie Chen, Bao Guo Shen
• Keywords: Blank Part, Edge Detection, Feature Point, Location Information, Shape, Stereoscopic Vision

References

[1] Belhumeur, Peter N. Bayesian approach to binocular stereopsis. International Journal of Computer Vision, v 19, n 3, pp.237-260 (1996).

[2] Geiger. Davi. Dynamic programming for detecting, tracking, and matching deformable contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, v 17, n 3, pp.294-302(1995).

DOI: 10.1109/34.368194

[3] Ishikawa, Hiroshi , Geiger. Davi. Segmentation by grouping junctions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.125-131(1998).

DOI: 10.1109/cvpr.1998.698598

[4] Bobick, A.F. . The recognition of human movement using temporal templates. Transactions on Pattern Analysis and Machine Intelligence, v 23, n 3, pp.257-267(2001).

DOI: 10.1109/34.910878

[5] Kang, Jian-Xin. A hybrid method combining Hausdorff distance, genetic algorithm and simulated annealing algorithm for image matching. ICCMS 2010 - 2010 International Conference on Computer Modeling and Simulation, v 2, pp.435-439(2010).

DOI: 10.1109/iccms.2010.136

[6] Sebe N, Lew M S. Texture features for content-Based retrieval. Principles of Visual Information Retrieval. Springer. Ch. 3, P51-85(2001).

DOI: 10.1007/978-1-4471-3702-3_3

[7] Guying Zhao, Pietikainen M. Local Binary Pattern Descriptors for Dynamic Texture Recognition; Pattern Recognition. ICPR 2006. 18th International Conference on Volume2，Page(s): 211-214(2006).

DOI: 10.1109/icpr.2006.768

[8] Jee Haeseong J，Campbell R Ian. Internet-based design visualization for layered manufacturing[J]. Concurrent Engineering Research and Applications, v 11, n 2, June, pp.151-158(2003).

DOI: 10.1177/1063293x03035389

[9] Song L G. Design and implementation of a virtual information system for agile manufacturing[J]. IIE Transaction, 29: 839-857(1997).

DOI: 10.1080/07408179708966406