Position estimation of autonomous guided vehicle
This thesis presents the development and application of a sensor fusion algorithm in positioning. The theoretical background behind the algorithm is based on the extended Kalman filter. By merging information from different sensors such as the Differential Global Positioning System (DGPS), rate g...
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sg-ntu-dr.10356-42962023-07-04T15:58:25Z Position estimation of autonomous guided vehicle Goh, Ching Tard. Wang, Han School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics This thesis presents the development and application of a sensor fusion algorithm in positioning. The theoretical background behind the algorithm is based on the extended Kalman filter. By merging information from different sensors such as the Differential Global Positioning System (DGPS), rate gyroscope and odometers, the filter is able to predict optimally the position and orientation of a 2-wheel steerable vehicle. In the filter, an enhanced kinematic process or vehicle model that accounts for the side slips experienced at the vehicle wheels is employed. These slip parameters that conform to the angles between the actual translated and pointed directions of the vehicle tires can affect the accuracy and consistency of the estimation system. Comparison between the enhanced model and another (without the slip consideration and based on pure kinematics) indicates improvements in the estimations as well as the orientation rate innovations with the slip compensation. Master of Engineering 2008-09-17T09:48:47Z 2008-09-17T09:48:47Z 1999 1999 Thesis http://hdl.handle.net/10356/4296 Nanyang Technological University application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics Goh, Ching Tard. Position estimation of autonomous guided vehicle |
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This thesis presents the development and application of a sensor fusion algorithm in
positioning. The theoretical background behind the algorithm is based on the extended
Kalman filter. By merging information from different sensors such as the Differential
Global Positioning System (DGPS), rate gyroscope and odometers, the filter is able to
predict optimally the position and orientation of a 2-wheel steerable vehicle. In the
filter, an enhanced kinematic process or vehicle model that accounts for the side slips
experienced at the vehicle wheels is employed. These slip parameters that conform to
the angles between the actual translated and pointed directions of the vehicle tires can
affect the accuracy and consistency of the estimation system. Comparison between the
enhanced model and another (without the slip consideration and based on pure
kinematics) indicates improvements in the estimations as well as the orientation rate
innovations with the slip compensation. |
author2 |
Wang, Han |
author_facet |
Wang, Han Goh, Ching Tard. |
format |
Theses and Dissertations |
author |
Goh, Ching Tard. |
author_sort |
Goh, Ching Tard. |
title |
Position estimation of autonomous guided vehicle |
title_short |
Position estimation of autonomous guided vehicle |
title_full |
Position estimation of autonomous guided vehicle |
title_fullStr |
Position estimation of autonomous guided vehicle |
title_full_unstemmed |
Position estimation of autonomous guided vehicle |
title_sort |
position estimation of autonomous guided vehicle |
publishDate |
2008 |
url |
http://hdl.handle.net/10356/4296 |
_version_ |
1772827465728655360 |