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Three methods of training multi-layer perceptrons to model a robot sensor

Published online by Cambridge University Press:  09 March 2009

D. T. Pham  and
S. Sagiroglu
D. T. Pham
Affiliation:
Intelligent Systems Laboratory, Systems Engineering Division, School of Engineering, University of Wales College of Cardiff, P.O. Box 917, Cardiff, CF2 1XH (UK)
S. Sagiroglu
Affiliation:
Intelligent Systems Laboratory, Systems Engineering Division, School of Engineering, University of Wales College of Cardiff, P.O. Box 917, Cardiff, CF2 1XH (UK)
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Summary

This paper discusses three methods of training multi-layer perceptrons (MLPs) to model a six-degrees-of- freedom inertial sensor. Such a sensor is designed for use with a robot to determine the location of objects it has to pick up. The sensor operates by measuring parameters related to the inertia of an object and computing its location from those parameters. MLP models are employed for part of the computation. They are trained to output the orientation of the object in response to an input pattern that includes the period of natural vibration of the sensor on which the object rests. After reviewing the working principle of the sensor, the paper describes the three MLP training methods (backpropagation, optimisation using the Levenberg-Marquardt algorithm, evolution based on the genetic algorithm) and presents the experimental results obtained.

Keywords

Neural networksMulti-layer perceptronsBackpropagation; Genetic algorithmLevenberg-Marquardt algorithmInertial sensorRobot sensor.

Information

Type
Articles
Information
Robotica , Volume 13 , Issue 5 , September 1995 , pp. 531 - 538
Copyright
Copyright © Cambridge University Press 1995

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References

1.Pham, D.T. and Dissanayake, M.W.M.G., “Feasibility Study of a Vibratory Sensor for Locating 3D-Objects” Proc. 25th Int. Machine Tool Design and Research Conference,Birmingham, UK(1985) pp. 210–211.Google Scholar
2.Pham, D.T. and Menendez, J., “Development of a Six-Degree-of-Freedom Vibratory Device for Locating Objects” Int. J. of Machine Tools and Manufacture 28, No. 1, 197–205 (1988).CrossRefGoogle Scholar
3.Pham, D.T. and Hafeez, K., “A New Technique for Determining the Location of 3-D Objects Using a Transputer-Controlled Inertial Sensor” Int. J. Machine Tools Manufacture 33, No. 5, 741–751 (1991).CrossRefGoogle Scholar
4.Pham, D.T., Hu, H. and Pote, J., “A Transputer-Based System for Locating Parts and Controlling an Industrial Robot” Robotica 8, Part 2, 97–103 (1989).CrossRefGoogle Scholar
5.Pham, D.T. and Hafeez, K., “Fuzzy Qualitative Model of a Robot Sensor for Locating Three-Dimensional Objects” Robotica, 10, Part 6, 555–562 (1992).CrossRefGoogle Scholar
6.Pham, D.T. and Hafeez, K., “Improving the Accuracy of a Vibratory Sensor Using Kalman Filtering” Robotica 11, Part 2, 129–138 (1993).CrossRefGoogle Scholar
7.Pham, D.T. and Hafeez, K., “Dynamic Modelling of a Robot Sensor” Int. J. Mathematical and Computer Modelling 14, 456–462 (1990).CrossRefGoogle Scholar
8.Pestel, E.C. and Leckie, F.A., Matrix Methods in Elasto Mechanics(McGraw Hill, New York, 1963).Google Scholar
9.Pham, D.T. and Heginbotham, W.B. (eds.), Robot Grippers (Springer-Verlag, Berlin, 1986).Google Scholar
10.Rumelhart, D.E. and McClelland, J.L., Parallel Distributed Processing: Explorations in the Microstructure of Cognition 1 (MIT Press, Cambridge, MA, 1986).CrossRefGoogle Scholar
11.Levenberg, K., “A Method for the Solution of Certain Nonlinear Problems in Least Squares” Quart. Appl. Math. 2,164–168 (1944).CrossRefGoogle Scholar
12.Marquardt, D.W., “An Algorithm for Least-Squares Estimation of Nonlinear Parameters” J. Soc. Ind. Appl. Math. 11, 431–441 (1963).CrossRefGoogle Scholar
13.Holland, J.H., Adaptation in Natural and Artificial Systems (The University of Michigan Press, Ann Arbor, MI, 1975).Google Scholar
14.Goldberg, D.E., Genetic Algorithms in Search, Optimisation & Machine Learning (Addison-Wesley, USA, 1989).Google Scholar
15.Davis, L., Handbook of Genetic Algorithms (eds.), (Van Nostrand Reinhold, NY, 1991).Google Scholar
16.Grefenstette, J.J., “Optimisation of Control Parameters for Genetic Algorithms” IEEE Trans, on Systems, Man and Cybernetics SMC-16, 122–128 (1986).CrossRefGoogle Scholar