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PCA-based method to fuse behaviors from place characterization for robot navigation

Published online by Cambridge University Press:  19 June 2015

Alberto Poncela
Alberto Poncela*
Affiliation:
Dpto. Tecnología Electrónica, ETSI Telecomunicación, Universidad de Málaga, Campus de Teatinos, 29071, Málaga, Spain
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper presents a method to calculate the fusing rule among three reactive behaviors, Wall Following, Corridor Following and Door Crossing, from place characterization for robot navigation. The technique is supported by a local grid of the closest area to the robot, which is built from sonar readings. The contour of this grid is extracted, represented by its FFT and, finally, it is reduced to a short feature vector with a principal component analysis (PCA). This feature vector is used to decide the fusing rule among the three behaviors. The algorithm is very fast in terms of its time performance, being then valid to be used in robot navigation, since the robot would rapidly react to new situations. It has also been successfully tested in simulated and real environments, with a Pioneer robot equipped with eight frontal sonar sensors, both in manually driven tasks and autonomous navigation tasks, proving its feasibility and effectiveness.

Keywords

CharacterizationBehavior fusionRobot navigation
Type
Articles
Information
Robotica , Volume 35 , Issue 2 , February 2017 , pp. 254 - 270
Copyright
Copyright © Cambridge University Press 2015 

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