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 fr a me to fr a me 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.