BSc project from the last half year is finally finished. It's focus was to implement and compare control systems to make the robot drive a specified trajectory. The control systems were combinations of 2 state estimators and 4 controllers. The state estimators filter out cricket noise and improve the state update frequentie by using prediction and the odometry data provided by sensors on the robot. The controllers try to control the robot in such a way to follow a timestamped trajectory as accurately as possible by using the data from the state estimator.

The BSc project group implemented a Particle Filter and improved the Unscented Kalman filter that was already present. The implemented controllers are the Linear feedback-, Non Linear feedback-, Dynamic feedback- and a PD controller. Besides these control algorithms a complete new MATLAB framework to test them was build. Finally the different combinations were tested in a simulation environment. The most promissing were tested in the lab with a figure 8 test trajectory. The error with the trajectory was determined using a ceiling mounted vision recognition camera. 

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