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A Localisation and Navigation System for an Autonomous Wheel Loader

Robin Lilja

ISBN 10: 3845417080 / ISBN 13: 9783845417080
Published by LAP LAMBERT Academic Publishing
New Condition: New Soft cover
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Paperback. 104 pages. Dimensions: 8.7in. x 5.9in. x 0.2in.Autonomous vehicles are an emerging trend in robotics, seen in a vast range of applications and environments. In an ongoing industrial project a fully autonomous wheel loader is being developed. This work (Masters Thesis) proposes a solution to two of the main problems encountered when developing such vehicle - namely the problems of how to localise and navigate the vehicle in its task space. The proposed solution relies at its fulcrum - on the widely known and commonly accepted Kalman filter. Different vehicle models used by the Kalman filter for conducting sensor fusion are derived and evaluated on real datasets collected from different environments. In order to solve the problem of navigating the vehicle a waypoint-based path following algorithm is adopted. As a given objective by the industrial project the found solution is implemented and integrated into the real-time environment of a full size autonomous wheel loader, and where the final system is demonstrated as a proof of concept for utilising autonomous wheel loaders for repetitive work tasks. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Bookseller Inventory # 9783845417080

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Bibliographic Details

Title: A Localisation and Navigation System for an ...

Publisher: LAP LAMBERT Academic Publishing

Binding: Paperback

Book Condition:New

Book Type: Paperback

About this title

Synopsis:

Autonomous vehicles are an emerging trend in robotics, seen in a vast range of applications and environments. In an ongoing industrial project a fully autonomous wheel loader is being developed. This work (Master’s Thesis) proposes a solution to two of the main problems encountered when developing such vehicle - namely the problems of how to localise and navigate the vehicle in its task space. The proposed solution relies – at its fulcrum - on the widely known and commonly accepted Kalman filter. Different vehicle models used by the Kalman filter for conducting sensor fusion are derived and evaluated on real datasets collected from different environments. In order to solve the problem of navigating the vehicle a waypoint-based path following algorithm is adopted. As a given objective by the industrial project the found solution is implemented and integrated into the real-time environment of a full size autonomous wheel loader, and where the final system is demonstrated as a proof of concept for utilising autonomous wheel loaders for repetitive work tasks.

About the Author:

Robin Lilja has been studying at Malardalen University (MDU, Sweden) and the University of Western Australia (UWA). In 2011 he graduated from MDU with the degree of Master of Science in Robotics Engineering. Currently he is working with real-time communication systems in the next generation of high-speed trains.

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