Image Invariant Robot Navigation Based on Self Organising Neural Place Codes

Kaustubh Chokshi, Stefan Wermter, Christo Panchev, Kevin Burn

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

For a robot to be autonomous it must be able to navigate independently within an environment. The overall aim of this paper is to show that localisation can be performed even without having a pre-defined map given to the robot by humans. In nature place cells are brain cells that respond to the environment the animal is in. In this paper we present a model of place cells based on Self Organising Maps. We also show how image invariance can improve the performance of the place cells and make the model more robust to noise. The incoming visual stimuli are interpreted by means of neural networks and they respond only to a specific combination of visual landmarks. The activities of these neural networks implicitly represent environmental properties like distance and orientation to the visual cues. Unsupervised learning is used to build the computational model of hippocampal place cells. After training, a robot can localise itself within a learned environment.
Original languageEnglish
Title of host publicationBiomimetic Neural Learning for Intelligent Robots
Subtitle of host publicationIntelligent Systems, Cognitive Robotics, and Neuroscience
EditorsStefan Wermter, Gunther Palm, Mark Elshaw
PublisherSpringer Verlag
Pages88-106
Number of pages19
Edition1
ISBN (Electronic)978-3-540-31896-5
ISBN (Print)978-3-540-27440-7
DOIs
Publication statusPublished - 2005
Externally publishedYes

Fingerprint Dive into the research topics of 'Image Invariant Robot Navigation Based on Self Organising Neural Place Codes'. Together they form a unique fingerprint.

Cite this