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Robust world-modelling and navigation in a real world

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Date

1996

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Abstract

This article will discuss a qualitative, topological and robust world-modelling technique with special regard to navigation tasks for mobile robots operating in unknown environments. As a central aspect, the reliability regarding error-tolerance and stability will be emphasized. Benefits and problems involved in exploration as well as in navigation tasks are discussed. The proposed method demands very low constraints for the kind and quality of the employed sensors as well as for the kinematic precision of the utilized mobile platform. Hard real-time constraints can be handled due to the low computational complexity. The principal discussions are supported by real-world experiments with the mobile robot 'ALICE'.

Description

Keywords

artificial neural networks, mobile robots, navigation, self-organization, world-modelling

Citation

URI

https://dspace-test.anu.edu.au/handle/1885/733746612

Collections

DevOps Pure Testing

Source

Neurocomputing

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Article

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DOI

10.1016/0925-2312(95)00097-6

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