dc.contributor.authorGoh, Ching Tard.en_US
dc.description.abstractThis 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.en_US
dc.rightsNanyang Technological Universityen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics
dc.titlePosition estimation of autonomous guided vehicleen_US
dc.contributor.supervisorWang, Hanen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Engineeringen_US

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