Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-18T23:43:33.312Z Has data issue: false hasContentIssue false

A system for robot manipulation of electrical wires using vision

Published online by Cambridge University Press:  09 March 2009

David Vernon
Affiliation:
Department of Computer Science, Trinity College, Dublin, Ireland

Summary

A prototype robot system for automated handling of flexible electrical wires of variable length is described. The handling process involves the selection of a single wire from a tray of many, grasping the wire close to its end with a robot manipulator, and either placing the end in a crimping press or, for tinning applications, dipping the end in a bath of molten solder. This system relies exclusively on the use of vision to identify the position and orientation of the wires prior to their being grasped by the robot end-effector. Two distinct vision algorithms are presented. The first approach utilises binary imaging techniques and involves object segmentation by thresholding followed by thinning and image analysis. An alternative general-purpose approach, based on more robust grey-scale processing techniques, is also described. This approach relies in the analysis of object boundaries generated using a dynamic contour-following algorithm. A simple Robot Control Language (RCL) is described which facilitates robot control in a Cartesian frame of reference and object description using frames (homogeneous transformations). The integration of this language with the robot vision system is detailed, and, in particular, a camera model which compensates for both photometric distortion and manipulator inaccuracies is presented. The system has been implemented using conventional computer architectures; average sensing cycle times of two and six seconds have been achieved for the grey-scale and binary vision algorithms, respectively.

Type
Article
Copyright
Copyright © Cambridge University Press 1990

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Mujtaba, M., Motion Sequencing of Manipulators (Report No. STAN-CS-82–917, Stanford University, 1982).Google Scholar
2.Simons, G.L., Robots in Industry (NCC Publications, Manchester, England, 1980).Google Scholar
3.Guo, H., Yachida, M., and Tsuji, S., “Three-dimensional Measurement of Many Line-like ObjectsAdvanced Robotics 1, No. 2, 117130 (1986).CrossRefGoogle Scholar
4.Agin, G., “Computer Vision Systems for Industrial Inspection and AssemblyComputer 13, No. 5, 1120 (1980).CrossRefGoogle Scholar
5.Kasvand, T., “Experiments on Automatic Extraction of Paper Pulp FibresProc. 4th International Joint Conference on Pattern Recognition958960 (1978).Google Scholar
6.Dixon, R.N. and Taylor, C.J., “Automated Asbestos Fibre Counting In: Machine Aided Image Analysis (Institute of Physics, Conference Series No. 44), 178185 (1978).Google Scholar
7.Chen, M.J. and Milgram, D., “A Development System for Machine VisionIEEE Computer Society Conference on Pattern Recognition and Image Processing512517 (1982).Google Scholar
8.Cunningham, R., “Segmenting Binary ImagesRobotics Age 3, No. 4, 419 (1981).Google Scholar
9.Bolles, R.C. and Cain, R.A., “Recognizing and Locating Partially Visable Objects: The Local-Feature-Focus MethodInt. J. Robotics Research 1, No. 3, 5782 (1982).CrossRefGoogle Scholar
10.Bolles, R.C. and Cain, R.A., “Recognising and Locating Partially Visable WorkpiecesProc. IEEE Computer Society Conference on Pattern Recognition and Image Processing498503 (1982).Google Scholar
11.Danielsson, P.-E., “An Improved Segmentation and Coding Algorithms for Binary and Non-Binary ImagesIBM J. Research and Development 26, No. 6, 698707 (1982).CrossRefGoogle Scholar
12.Segen, J., “Locating Randomly Oriented Objects from Partial ViewProceedings of SPIE 449, 676684 (1983).Google Scholar
13.Knoll, T.F. and Jain, R.C., “Recognising Partially Visible Objects Using Feature Indexed HypothesesIEEE J. Robotics and Automation RA-2, No. 1, 313 (1986).Google Scholar
14.Tanimoto, S.L., “Image Data Structures” In: Structured Computer Vision (Academic Press, New York 1980) pp. 3155.Google Scholar
15.Pratt, W.K., Digital Image Processing (Wiley, New York, 1978).Google Scholar
16.Ballard, D.H. and Brown, C.M., Computer Vision (Prentice-Hall, New Jersey, 1982).Google Scholar
17.Cooper, D. and Sung, F., “Multiple-Window Parallel Adaptive Boundary Finding in Computer VisionIEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-5, No. 3, 299316 (1983).CrossRefGoogle Scholar
18.Weszka, J.S., “A Survey of Thresholding Selection TechniquesComputer Graphics and Image Processing 7, 259265 (1978).CrossRefGoogle Scholar
19.Katz, Y.H., “Pattern Recognition of Meteorological Satellite Cloud Photography” Proc. Third Symposium on Remote Sensing of the Environment, Institute of Science and Technology, University of Michigan 173214 (1965).Google Scholar
20.Barrett, W.A., “An Iterative Algorithm for Multiple Threshold DetectionProc. IEEE Computer Society Conference on Pattern Recognition and Image Processing,Dallas,273278 (1981).Google Scholar
21.Gallus, G. and Neurath, P.W., “Improved Computer Chromosome Analysis Incorporating Preprocessing and Boundary AnalysisPhys. Med. Biol. 15, No. 3, 435445 (1970).CrossRefGoogle ScholarPubMed
22.Weszka, J.S. and Rosenfeld, A., “A Threshold Selection TechniqueIEEE Transactions on Computers CC-23, No.12, 13221327 (1974).CrossRefGoogle Scholar
23.Marr, D. and Hildreth, E., “Theory of Edge DetectionProceedings of the Royal Society of London B207, 187217 (1980).Google Scholar
24.Levi, G. and Montanari, U., “A Gray-Weighted SkeletonInformation and Control 17, 6291 (1970).CrossRefGoogle Scholar
25.Serra, J., “Images et Morphologie MathematiqueLa Recherche 14, No. 144, 723732 (1983).Google Scholar
26.Motzkin, Th., “Sur Quelques Proprietes Caracteristiques des Ensembles Bornes Non ConvexesAtti. Acad. Naz. Lincel 21, 773779 (1935).Google Scholar
27.Blum, H., “An Associative Machine for dealing with the Visual Field and some of its related PropertiesBiol. Prot. and Synth. Syst. l, 244260 (1962).Google Scholar
28.Blum, H., “A Transformation for Extracting New Descriptors of Shape” Models for the Perception of Speech and Visual Form (MIT Press Cambridge, MA. 1967) pp. 153171.Google Scholar
29.Mott-Smith, J.C., “Medial Axis Transformations” In: Picture Processing and Psychopictorics (Academic Press, New York, 1970) pp. 267283.Google Scholar
30.Pavlidis, T., “A Review of Algorithms for Shape AnalysisComputer Graphics and Image Processing 7, 243258 (1978).CrossRefGoogle Scholar
31.Wall, R., Klinger, A., and Harami, S., “Algorithm for Computing the Medial Axis Transform and its Inverse” Proc. of the 1977 Workshop on Picture Data Description and Management (Proceedings 77CH1187–4C, IEEE Computer Society, Picastaway, New Jersey) 121122 (1977).CrossRefGoogle Scholar
32.Tamura, H., “A Comparison of Line-Thinning Algorithms from Digital Geometry ViewpointProc. 4th International Joint Conference on Pattern Recognition715719 (1978).Google Scholar
33.Rosenfeld, A. and Kak, A., Digital Picture Processing (Academic Press, New York, 1982).Google Scholar
34.Lee, H.U. and Fu, K.S., “The GLGS Image Representation and its Application to Preliminary Segmentation and Pre-attentive Visual SearchIEEE Computer Society Conference on Pattern Recognition and Image Processing,256261 (1981).Google Scholar
35.Hanson, A.R. and Riseman, E.M., “Segmentation of Natural Scenes” In: Computer Vision Systems (Academic Press, New York, 1978).Google Scholar
36.Martin, W.N. and Aggarwal, J.K., “Survey-Dynamic Scene AnalysisComputer Graphics and Image Processing 7, No. 3, 356374 (1978).CrossRefGoogle Scholar
37.Jain, R. and Haynes, S., “Imprecision in Computer VisionComputer 15(8), 3948 (1982).CrossRefGoogle Scholar
38.Pau, L.F., “Approaches to Industrial Image Processing and their LimitationsElectronics and Power, 02, 135140 (1984).CrossRefGoogle Scholar
39.Freeman, H., “On the Encoding of Arbitrary Geometric Configurations”, IRE Trans. on Electronic Computers 260268 (1961).CrossRefGoogle Scholar
40.Roberts, L.G., “Machine Perception of Three-Dimensional Solids” In: Optical and Electro-Optical Information Processing (MIT Press, Cambridge, Massachusetts 1965), p. 159197.Google Scholar
41.Paul, R., Robot Manipulators: Mathematics, Programming, and Control (MIT Press, Cambridge, Massachusetts, 1981).Google Scholar
42.Hall, E.L., Computer Image Processing and Recognition (Academic Press, New York, 1979).Google Scholar
43.Lozano-Perez, T., “Robot ProgrammingMIT AI Lab, AI Memo 698 (1982).Google Scholar
44.Bonner, S. and Shin, K.G., “A Comparative Study of Robot LanguagesComputer 15, No. 12, 8296 (1982).CrossRefGoogle Scholar