Advanced 3D Imaging Technology for Autonomous Manufacturing Systems

Today’s markets and economies are becoming increasingly volatile, unpredictable, they are changing radically and even the innovation speed is accelerating. Manufacturing and production technology and systems must keep pace with this trend. The impact of novel innovative 3D imaging technology to counter these radical changes is exemplarily shown on the robot paint process. One has to keep in mind that investments in automatic painting lines are considerably high and as the painting line often is the bottleneck in production, it is imperative to prevent nonproductive times and maximize the use of the expensive equipment. Highly flexible, scalable and user-friendly production equipment is needed, including robotic systems for painting – a common process in production.

[1]  Xuejun Sheng,et al.  Surface reconstruction and extrapolation from multiple range images for automatic turbine blades repair , 1998, IECON '98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200).

[2]  Andrew W. Fitzgibbon,et al.  An Experimental Comparison of Range Image Segmentation Algorithms , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  D. Rokossa,et al.  Process-oriented approach to an efficient off-line programming of industrial robots , 1998, IECON '98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200).

[4]  Robert B. Fisher,et al.  Recognition of Complex 3-D Objects from Range Data , 1993 .

[5]  Anil K. Jain,et al.  Segmentation and Classification of Range Images , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Ralph R. Martin,et al.  Robust Segmentation of Primitives from Range Data in the Presence of Geometric Degeneracy , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Henrik Gordon Petersen,et al.  A new method for estimating parameters of a dynamic robot model , 2001, IEEE Trans. Robotics Autom..

[8]  Patrick J. Flynn,et al.  A Survey Of Free-Form Object Representation and Recognition Techniques , 2001, Comput. Vis. Image Underst..

[9]  Adrian Hilton,et al.  Marching triangles: range image fusion for complex object modelling , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[10]  Andrew E. Johnson,et al.  Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Henrik Gordon Petersen,et al.  Task curve planning for painting robots. I. Process modeling and calibration , 1996, IEEE Trans. Robotics Autom..

[12]  Kwan S. Kwok,et al.  Rapid 3-D digitizing and tool path generation for complex shapes , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[13]  Michael Brady,et al.  Saliency, Scale and Image Description , 2001, International Journal of Computer Vision.

[14]  Patrick J. Flynn,et al.  Eigenshapes for 3D object recognition in range data , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[15]  Yonghua Chen,et al.  A robotic system for rapid prototyping , 1997, Proceedings of International Conference on Robotics and Automation.

[16]  Markus Vincze,et al.  A method for automatic spray painting of unknown parts , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[17]  Angel P. del Pobil,et al.  Practical Motion Planning in Robotics: Current Approaches and Future Directions , 1998 .

[18]  Tapas Kanungo,et al.  Hierarchical organization of appearance-based parts and relations for object recognition , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[19]  Hiroshi Murase,et al.  Visual learning and recognition of 3-d objects from appearance , 2005, International Journal of Computer Vision.