Title:
ApplicationsoftheKalmanFilterslgorithmtorobotloca
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Description: To model the robot position we wish to know its x and y coordinates and its
orientation. These three parameters can be combined into a vector called a state
variable vector. The robot uses beacon distance and angle measurements and
locomotion information about how far it has walked to calculate its position. As with
any real system, these measurements include a component of error (or noise). If
trigonometry is used to calculate the robot s position it can have a large error and
can change significantly from frame to frame depending on the measurement at the
time. This makes the robot appear as if it is "jumping" around the field. The Kalman
Filter is a smarter way to integrate measurement data into an estimate by
recognising that measurements are noisy and that sometimes they should ignored
or have only a small effect on the state estimate.
File list (Check if you may need any files):
Applications of the Kalman Filter slgorithm to robot localization and.pdf
