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Autonomous Path Following Using Convolutional Networks
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
2012 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Autonomous vehicles have many application possibilities within many different fields like rescue missions, exploring foreign environments or unmanned vehicles etc. For such system to navigate in a safe manner, high requirements of reliability and security must be fulfilled.

This master's thesis explores the possibility to use the machine learning algorithm convolutional network on a robotic platform for autonomous path following. The only input to predict the steering signal is a monochromatic image taken by a camera mounted on the robotic car pointing in the steering direction. The convolutional network will learn from demonstrations in a supervised manner.

In this thesis three different preprocessing options are evaluated. The evaluation is based on the quadratic error and the number of correctly predicted classes. The results show that the convolutional network has no problem of learning a correct behaviour and scores good result when evaluated on similar data that it has been trained on. The results also show that the preprocessing options are not enough to ensure that the system is environment dependent.

Place, publisher, year, edition, pages
2012. , 44 p.
Keyword [en]
Machine Learning, Autonomous Vehicle, Convolutional Network, Path Following
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-78670ISRN: LiTH-ISY-EX--12/4577--SEOAI: oai:DiVA.org:liu-78670DiVA: diva2:534610
Subject / course
Computer Vision Laboratory
Uppsok
Technology
Available from: 2012-06-18 Created: 2012-06-18 Last updated: 2012-06-18Bibliographically approved

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Examensarbete(2297 kB)337 downloads
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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
  • html
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