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<title>UTas ePrints - A Simplified Fuzzy-Logic Control System Approach to Obstacle Avoidance combining Stereoscopic Vision and Sonar</title>
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<meta content="Stereoscopic vision is a technique for calculating the depths of objects in a scene from two images. Ultrasonic ranging is a well-established technique for estimating the distance to objects by bouncing an acoustic pulse off the object and measuring the time-of-flight. As with any types of sensors, these techniques each have their associated strengths and weaknesses. Therefore it is desirable
to be able to use both sensor types simultaneously on a robot such that the benefits of the techniques can each be taken advantage of.
Effective obstacle avoidance is an important challenge in the field of robotics that is integral to achieving the goal of fully autonomous mobile robots. To achieve such behaviour a control system is required for directing the robot on the basis of sensor inputs.
This study presents a simplified fuzzy logic control system that differs from the standard fuzzy logic system in the way that fuzzy sets are generated. The control layers of the system dynamically create fuzzy sets on-the-fly when called upon to do so. The developed control system is used to show that there are benefits to combining stereoscopic vision and sonar for robot obstacle avoidance compared against using these sensors in isolation." name="eprints.abstract" />
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<meta content="Stereoscopic vision is a technique for calculating the depths of objects in a scene from two images. Ultrasonic ranging is a well-established technique for estimating the distance to objects by bouncing an acoustic pulse off the object and measuring the time-of-flight. As with any types of sensors, these techniques each have their associated strengths and weaknesses. Therefore it is desirable
to be able to use both sensor types simultaneously on a robot such that the benefits of the techniques can each be taken advantage of.
Effective obstacle avoidance is an important challenge in the field of robotics that is integral to achieving the goal of fully autonomous mobile robots. To achieve such behaviour a control system is required for directing the robot on the basis of sensor inputs.
This study presents a simplified fuzzy logic control system that differs from the standard fuzzy logic system in the way that fuzzy sets are generated. The control layers of the system dynamically create fuzzy sets on-the-fly when called upon to do so. The developed control system is used to show that there are benefits to combining stereoscopic vision and sonar for robot obstacle avoidance compared against using these sensors in isolation." name="DC.description" />
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<h1 class="ep_tm_pagetitle">A Simplified Fuzzy-Logic Control System Approach to Obstacle Avoidance combining Stereoscopic Vision and Sonar</h1>
<p style="margin-bottom: 1em" class="not_ep_block"><span class="person_name">Grayston, Thomas Ian</span> (2006) <xhtml:em>A Simplified Fuzzy-Logic Control System Approach to Obstacle Avoidance combining Stereoscopic Vision and Sonar.</xhtml:em> Honours thesis, University of Tasmania.</p><p style="margin-bottom: 1em" class="not_ep_block"></p><table style="margin-bottom: 1em" class="not_ep_block"><tr><td valign="top" style="text-align:center"><a onmouseover="EPJS_ShowPreview( event, 'doc_preview_796' );" href="http://eprints.utas.edu.au/791/1/Thesis.pdf" onmouseout="EPJS_HidePreview( event, 'doc_preview_796' );"><img alt="[img]" src="http://eprints.utas.edu.au/style/images/fileicons/application_pdf.png" class="ep_doc_icon" border="0" /></a><div class="ep_preview" id="doc_preview_796"><table><tr><td><img alt="" src="http://eprints.utas.edu.au/791/thumbnails/1/preview.png" class="ep_preview_image" border="0" /><div class="ep_preview_title">Preview</div></td></tr></table></div></td><td valign="top"><a href="http://eprints.utas.edu.au/791/1/Thesis.pdf"><span class="ep_document_citation">PDF (Thesis without appendixes)</span></a> - Requires a PDF viewer<br />2583Kb</td></tr></table><div class="not_ep_block"><h2>Abstract</h2><p style="padding-bottom: 16px; text-align: left; margin: 1em auto 0em auto">Stereoscopic vision is a technique for calculating the depths of objects in a scene from two images. Ultrasonic ranging is a well-established technique for estimating the distance to objects by bouncing an acoustic pulse off the object and measuring the time-of-flight. As with any types of sensors, these techniques each have their associated strengths and weaknesses. Therefore it is desirable
to be able to use both sensor types simultaneously on a robot such that the benefits of the techniques can each be taken advantage of.
Effective obstacle avoidance is an important challenge in the field of robotics that is integral to achieving the goal of fully autonomous mobile robots. To achieve such behaviour a control system is required for directing the robot on the basis of sensor inputs.
This study presents a simplified fuzzy logic control system that differs from the standard fuzzy logic system in the way that fuzzy sets are generated. The control layers of the system dynamically create fuzzy sets on-the-fly when called upon to do so. The developed control system is used to show that there are benefits to combining stereoscopic vision and sonar for robot obstacle avoidance compared against using these sensors in isolation.</p></div><table style="margin-bottom: 1em" cellpadding="3" class="not_ep_block" border="0"><tr><th valign="top" class="ep_row">Item Type:</th><td valign="top" class="ep_row">Thesis (Honours)</td></tr><tr><th valign="top" class="ep_row">Keywords:</th><td valign="top" class="ep_row">sensor fusion, sonar, vision, obstacle avoidance, fuzzy logic, robotics</td></tr><tr><th valign="top" class="ep_row">Subjects:</th><td valign="top" class="ep_row"><a href="http://eprints.utas.edu.au/view/subjects/280212.html">280000 Information, Computing and Communication Sciences > 280200 Artificial Intelligence and Signal and Image Processing > 280212 Neural Networks, Genetic Alogrithms and Fuzzy Logic</a><br /><a href="http://eprints.utas.edu.au/view/subjects/280209.html">280000 Information, Computing and Communication Sciences > 280200 Artificial Intelligence and Signal and Image Processing > 280209 Intelligent Robotics</a></td></tr><tr><th valign="top" class="ep_row">ID Code:</th><td valign="top" class="ep_row">791</td></tr><tr><th valign="top" class="ep_row">Deposited By:</th><td valign="top" class="ep_row"><span class="ep_name_citation"><span class="person_name">Prof Arthur Sale</span></span></td></tr><tr><th valign="top" class="ep_row">Deposited On:</th><td valign="top" class="ep_row">22 Feb 2007</td></tr><tr><th valign="top" class="ep_row">Last Modified:</th><td valign="top" class="ep_row">09 Jan 2008 02:30</td></tr><tr><th valign="top" class="ep_row">ePrint Statistics:</th><td valign="top" class="ep_row"><a target="ePrintStats" href="/es/index.php?action=show_detail_eprint;id=791;">View statistics for this ePrint</a></td></tr></table><p align="right">Repository Staff Only: <a href="http://eprints.utas.edu.au/cgi/users/home?screen=EPrint::View&eprintid=791">item control page</a></p>
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