Task identification and characterisation in mobile robotics through non-linear modelling

The lack of a theory-based design methodology for mobile robot control programs means that control programs have to be developed through an empirical trial-and-error process. This can be costly, time consuming and error prone. In this paper we show how to develop a theory of robot-environment interaction, which would overcome the above problem. We show how we can model a mobile robot's task (so-called ''task identification'') using non-linear polynomial models (NARMAX), which can subsequently be formally analysed using established mathematical methods. This provides an understanding of the underlying phenomena governing the robot's behaviour. Apart from the paper's main objective of formally analysing robot-environment interaction, the task identification process has further benefits, such as the fast and convenient cross-platform transfer of robot control programs (''Robot Java''), parsimonious task representations (memory issues) and very fast control code execution times.

[1]  S. Billings,et al.  Orthogonal parameter estimation algorithm for non-linear stochastic systems , 1988 .

[2]  W. J. Langford.  Statistical Methods , 1959, Nature.

[3]  S. A. Billings,et al.  Visual Task Identification Using Polynomial Models ∗ , 2006 .

[4]  Theocharis Kyriacou,et al.  Route Training in Mobile Robotics through System Identification , 2008 .

[5]  Peter K. McGregor,et al.  Asking Questions in Biology: Design, Analysis and Presentation in Practical Work , 1993 .

[6]  Joan Cabestany,et al.  Biological and Artificial Computation: From Neuroscience to Technology , 1997, Lecture Notes in Computer Science.

[7]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[8]  O. L. R. Jacobs,et al.  Trends and progress in system identification , 1982, Autom..

[9]  Theocharis Kyriacou,et al.  ROBOT PROGRAMMING THROUGH A COMBINATION OF MANUAL TRAINING AND SYSTEM IDENTIFICATION , 2005 .

[10]  Ulrich Nehmzow.  Mobile Robotics: A Practical Introduction , 2003 .

[11]  P. Eykhoff.  System Identification Parameter and State Estimation , 1974 .

[12]  Sheng Chen,et al.  Representations of non-linear systems: the NARMAX model , 1989 .

[13]  Stephen A. Billings,et al.  Robot learning through task identification , 2006, Robotics Auton. Syst..

[14]  Cornelius T. Leondes,et al.  Neural network systems techniques and applications , 1998 .

[15]  Senén Barro,et al.  Implementation of a Basic Reactive Behavior in Mobile Robotics Through Artificial Neural Networks , 1997, IWANN.

[16]  Stephen A. Billings,et al.  Training and Analysis of Mobile Robot Behaviour Through System Identification , 2005, CAEPIA.

[17]  Stephen A. Billings,et al.  Visual task identification and characterization using polynomial models , 2007, Robotics Auton. Syst..

[18]  S. Billings,et al.  Correlation based model validity tests for non-linear models , 1986 .

[19]  Patrick Brézillon,et al.  Lecture Notes in Artificial Intelligence , 1999 .

[20]  N. Hatton.  ASKING QUESTIONS , 1979, The Medical journal of Australia.

[21]  Peter K. McGregor,et al.  Asking questions in biology , 1993 .