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Unsupervised learning for mobile rob...

Giguere, Philippe.

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Unsupervised learning for mobile robot terrain classification .

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Unsupervised learning for mobile robot terrain classification ./
Author:	Giguere, Philippe.
Description:	214 p.
Notes:	Source: Dissertation Abstracts International, Volume: 72-07, Section: B, page: .
Contained By:	Dissertation Abstracts International72-07B.
Subject:	Engineering, Robotics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR72631
ISBN:	9780494726310

Unsupervised learning for mobile robot terrain classification .
Giguere, Philippe.

Unsupervised learning for mobile robot terrain classification . - 214 p.

Source: Dissertation Abstracts International, Volume: 72-07, Section: B, page: .

Thesis (Ph.D.)--McGill University (Canada), 2010.

In this thesis, we consider the problem of having a mobile robot autonomously learn to perceive differences between terrains. The targeted application is for terrain identification. Robust terrain identification can be used to enhance the capabilities of mobile systems, both in terms of locomotion and navigation. For example, a legged amphibious robot that has learned to differentiate sand from water can automatically select its gait on a beach: walking for sand, and swimming for water. The same terrain information can also be used to guide a robot in order to avoid specific terrain types.

ISBN: 9780494726310Subjects--Topical Terms:

1018454
Engineering, Robotics.

Unsupervised learning for mobile robot terrain classification .
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100 1 $a Giguere, Philippe. $3 1681719
245 1 0 $a Unsupervised learning for mobile robot terrain classification .
300 $a 214 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-07, Section: B, page: .
502 $a Thesis (Ph.D.)--McGill University (Canada), 2010.
520 $a In this thesis, we consider the problem of having a mobile robot autonomously learn to perceive differences between terrains. The targeted application is for terrain identification. Robust terrain identification can be used to enhance the capabilities of mobile systems, both in terms of locomotion and navigation. For example, a legged amphibious robot that has learned to differentiate sand from water can automatically select its gait on a beach: walking for sand, and swimming for water. The same terrain information can also be used to guide a robot in order to avoid specific terrain types.
520 $a The problem of autonomous terrain identification is decomposed into two sub-problems: a sensing sub-problem, and a learning sub-problem. In the sensing sub-problem, we look at extracting terrain information from existing sensors, and at the design of a new tactile probe. In particular, we show that inertial sensor measurements and actuator feedback information can be combined to enable terrain identification for a legged robot. In addition, we describe a novel tactile probe designed for improved terrain sensing. In the learning sub-problem, we discuss how temporal or spatial continuities can be exploited to perform the clustering of both time-series and images. Specifically, we present a new algorithm that can be used to train a number of classifiers in order to perform clustering when temporal or spatial dependencies between samples are present. We combine our sensing approach with this clustering technique, to obtain a computational architecture that can learn autonomously to differentiate terrains. This approach is validated experimentally using several different sensing modalities (proprioceptive and tactile) and with two different robotic platforms (on a legged robot named AQUA and a wheeled robot iRobot(TM) Create(TM)). Finally, we show that the same clustering technique, when combined with image information, can be used to define a new image segmentation algorithm.
590 $a School code: 0781.
650 4 $a Engineering, Robotics. $3 1018454
650 4 $a Artificial Intelligence. $3 769149
650 4 $a Computer Science. $3 626642
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710 2 $a McGill University (Canada). $3 1018122
773 0 $t Dissertation Abstracts International $g 72-07B.
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792 $a 2010
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