Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31108
Route Training in Mobile Robotics through System Identification

Authors: Roberto Iglesias, Theocharis Kyriacou, Ulrich Nehmzow, Steve Billings


Fundamental sensor-motor couplings form the backbone of most mobile robot control tasks, and often need to be implemented fast, efficiently and nevertheless reliably. Machine learning techniques are therefore often used to obtain the desired sensor-motor competences. In this paper we present an alternative to established machine learning methods such as artificial neural networks, that is very fast, easy to implement, and has the distinct advantage that it generates transparent, analysable sensor-motor couplings: system identification through nonlinear polynomial mapping. This work, which is part of the RobotMODIC project at the universities of Essex and Sheffield, aims to develop a theoretical understanding of the interaction between the robot and its environment. One of the purposes of this research is to enable the principled design of robot control programs. As a first step towards this aim we model the behaviour of the robot, as this emerges from its interaction with the environment, with the NARMAX modelling method (Nonlinear, Auto-Regressive, Moving Average models with eXogenous inputs). This method produces explicit polynomial functions that can be subsequently analysed using established mathematical methods. In this paper we demonstrate the fidelity of the obtained NARMAX models in the challenging task of robot route learning; we present a set of experiments in which a Magellan Pro mobile robot was taught to follow four different routes, always using the same mechanism to obtain the required control law.

Keywords: Mobile robotics, System Identification, non-linear modelling, NARMAX

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1349


[1] Theocharis Kyriacou Ulrich Nehmzow, Roberto Iglesias and Stephen A. Billings, "Robot learning through task indentification," J. Robotics and Autonomous Systems., Accepted.
[2] T. Kyriacou, U. Nehmzow, R. Iglesias, and S. Billings, "Cross-platform programming through system identification," in Proceedings of TAROS 2005, London, 2005.
[3] S. Billings and S. Chen, "The determination of multivariable nonlinear models for dynamical systems," in Neural Network Systems, Techniques and Applications, C. Leonides, Ed., pp. 231-278. Academic press, 1998.
[4] M. Korenberg, S. Billings, Y. P. Liu, and P. J. McIlroy, "Orthogonal parameter estimation algorithm for non-linear stochastic systems," International Journal of Control, vol. 48, pp. 193-210, 1988.
[5] G. W. Snedecor and W. G. Cochran, Statistical Methods, Iowa State Press, 8th edition, 1989.
[6] I.M. Sobol, "Sensitivity estimates for nonlinear mathematical models," Mathematical Modelling and Computational Experiment, vol. 1, pp. 407- 414, 1993.