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Robot navigation based on view sequences stored in a sparse distributed memory

Published online by Cambridge University Press:  26 July 2011

Mateus Mendes ,
A. Paulo Coimbra  and
Manuel M. Crisóstomo
Mateus Mendes*
Affiliation:
ESTGOH, Polytechnic Institute of Coimbra, R. General Santos Costa, 3400-124 Oliveira do Hospital, Portugal
A. Paulo Coimbra
Affiliation:
ESTGOH, Polytechnic Institute of Coimbra, R. General Santos Costa, 3400-124 Oliveira do Hospital, Portugal
Manuel M. Crisóstomo
Affiliation:
Institute of Systems and Robotics, Pólo II, Pinhal de Marrocos, 3030-290 Coimbra, Portugal
*
*Corresponding author. E-mail: [email protected]
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Summary

Robot navigation is a large area of research, where many different approaches have already been tried, including navigation based on visual memories. The Sparse Distributed Memory (SDM) is a kind of associative memory based on the properties of high-dimensional binary spaces. It exhibits characteristics, such as tolerance to noise and incomplete data, ability to work with sequences and the possibility of one-shot learning. Those characteristics make it appealing to use for robot navigation. The approach followed here was to navigate a robot using sequences of visual memories stored into a SDM. The robot makes intelligent decisions, such as selecting only relevant images to store, adjusting memory parameters to the level of noise and inferring new paths from the learnt trajectories. The method of encoding the information may influence the tolerance of the SDM to noise and saturation. This paper reports novel results of the limits of the model under different typical navigation problems. The SDM showed to be very robust to illumination and scenario changes, occlusion and saturation. An algorithm to build a topological map of the environment based on the visual memories is also described.

Keywords

Sparse distributed memorySDMRobot navigationData encodingVision-based navigation
Type
Articles
Information
Robotica , Volume 30 , Issue 4 , July 2012 , pp. 571 - 581
Copyright
Copyright © Cambridge University Press 2011

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