Visual task identification and characterization using polynomial models

O. Akanyeti*, T. Kyriacou, U. Nehmzow, R. Iglesias, S. A. Billings

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (SciVal)

Abstract

Developing robust and reliable control code for autonomous mobile robots is difficult, because the interaction between a physical robot and the environment is highly complex, subject to noise and variation, and therefore partly unpredictable. This means that to date it is not possible to predict robot behaviour based on theoretical models. Instead, current methods to develop robot control code still require a substantial trial-and-error component to the software design process. This paper proposes a method of dealing with these issues by (a) establishing task-achieving sensor-motor couplings through robot training, and (b) representing these couplings through transparent mathematical functions that can be used to form hypotheses and theoretical analyses of robot behaviour. We demonstrate the viability of this approach by teaching a mobile robot to track a moving football and subsequently modelling this task using the NARMAX system identification technique.

Original languageEnglish
Pages (from-to)711-719
Number of pages9
JournalRobotics and Autonomous Systems
Volume55
Issue number9
Early online date04 Jun 2007
DOIs
Publication statusPublished - 30 Sept 2007
Externally publishedYes

Keywords

  • Autonomous mobile robots
  • Polynomials
  • System identification

Fingerprint

Dive into the research topics of 'Visual task identification and characterization using polynomial models'. Together they form a unique fingerprint.

Cite this