Hostname: page-component-76fb5796d-x4r87 Total loading time: 0 Render date: 2024-04-25T12:16:58.209Z Has data issue: false hasContentIssue false

A novel and practical strategy for the precise chamferless robotic peg hole insertion

Published online by Cambridge University Press:  09 March 2009

H. Qiao
Affiliation:
Dept. of Mechanical and Manufacturing Engineering, De Montfort University, The Gateway, Leicester, LE19BH (UK)
B. S. Dalay
Affiliation:
Dept. of Mechanical and Manufacturing Engineering, De Montfort University, The Gateway, Leicester, LE19BH (UK)
R. M. Parkin
Affiliation:
Dept. of Mechanical and Manufacturing Engineering, De Montfort University, The Gateway, Leicester, LE19BH (UK)

Summary

The influence of the angle between the axes of the peg and hole (angular error) and the contact surface defects on the measurement of deviation (lateral error) have been carefully analysed. It has been shown that they would influence the measurement of the magnitude of deviation and even its direction. This phenomenon causes severe difficulty in the assembly operations. A novel strategy for the high-precision chamferless peg hold insertion with a wrist force sensor is presented. This strategy is constructed: (1) to obtain the relationship between the peg and hole from the force sensor signal when an angle between the axes of the peg and hole exists and defects of the contact surfaces are present, (2) to reduce the angular and lateral errors, (3) to achieve the precise chamferless robotic peg hole insertion. In this paper, the insertion can be obtained with a reasonably large range of initial conditions. The principle is to move and rotate the peg from an area having many geometric uncertainties to a new area, where the deviation of the peg and hole can be obtained.

Type
Articles
Copyright
Copyright © Cambridge University Press 1995

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.Nevins, J. L. and Whitney, D. E., “Assembly Research”, Automatica 16, 595613 (1980).CrossRefGoogle Scholar
2.Whitney, D. E., “Quasi-Static Assembly of Compliantly Supported Rigid PartsTrans. ASME J. Dyn. Sys. Meas. and Con. 104, 6577 (1982).CrossRefGoogle Scholar
3.Lane, J. D., “Evaluation of a Remote Centre Compliance DeviceAssembly Automation 1, No. 1 3646 (1980).CrossRefGoogle Scholar
4.Jeong, K. W. and Cho, H. S., “Development of a pneumatic vibratory wrist for robotic assemblyRobotica 7, part 1, 916 (1989).CrossRefGoogle Scholar
5.Cho, H. S., Warnecke, H. T. and Kweon, D. G., “Robotic Assembly: a synthesizing overviewRobotica 5, part 2, 153165 (1987).CrossRefGoogle Scholar
6.Badano, F., Betemps, M., Redarce, T. and Jutard, A., “Robotic Assembly by Slight Random MovementsRobotica 9, part 1, 2329 (1991)CrossRefGoogle Scholar
Badano, F. et al. , “Assembly of chamferless parts using a fast robot” Procs. 24th Int. Symp. on Industrial Robots, Tokyo, 11 4–6, 1993 (1993) pp. 8996.Google Scholar
7.Simons, J., Van Brussel, H., De Schutter, I. and Verhaert, J., “A Self-Learning Automation with Variable Resolution for High Precision Assembly by Industrial RobotsIEEE Trans. on Automatic Control AC-27, No. 5, 11091113 (1982).CrossRefGoogle Scholar
8.Takeyasu, K., Goto, T. & Inoyama, T., “Precision Insertion Control Robot and Its Applications” ASME J. Engineering for Industry 13131318 (Nov., 1976).CrossRefGoogle Scholar
9.Stokic, D. and Vukobratovic, M., “Simulation and Control Synthesis of Manipulator in Assembly Technical PartsASME J. Dynamics, Measurement and Control 101, 332338 (12., 1979).CrossRefGoogle Scholar
10.Simons, J., Van Brussel, H., De Schutter, J. and Verhaert, J.A Self-learning Automation with Variable Resolution for High Precision assembly by Industrial Robots'IEEE Trans. Automatic Control AC-27, No. 5, 11091113 (1982).CrossRefGoogle Scholar
11.Asada, H. and Hirai, S., “Towards a Symbolic-Level Force Feedback Recognition of Assembly Process States” Proc. 5th Int. Symp. of Robotics Research, Tokyo (1989) pp. 341346.Google Scholar
12.Asada, H., “Teaching and Learning of Compliance Using Neural Nets” Proc. IEEE Int. Conf. Robotics 7, 916 (1990).Google Scholar