Novelty Detection on a Mobile Robot Using Habituation
Abstract
In this paper a novelty filter is introduced which allows a robot operating in an un structured environment to produce a self-organised model of its surroundings and to detect deviations from the learned model. The environment is perceived using the rob ot's 16 sonar sensors. The algorithm produces a novelty measure for each sensor scan relative to the model it has learned. This means that it highlights stimuli which h ave not been previously experienced. The novelty filter proposed uses a model of hab ituation. Habituation is a decrement in behavioural response when a stimulus is pre sented repeatedly. Robot experiments are presented which demonstrate the reliable o peration of the filter in a number of environments.
Keywords
Cite
@article{arxiv.cs/0006007,
title = {Novelty Detection on a Mobile Robot Using Habituation},
author = {Stephen Marsland and Ulrich Nehmzow and Jonathan Shapiro},
journal= {arXiv preprint arXiv:cs/0006007},
year = {2007}
}
Comments
10 pages, 6 figures. In From Animals to Animats, The Sixth International Conference on Simulation of Adaptive Behaviour, Paris, 2000