The thesis presents a kinematic and dynamic model of the mobile robot platform derived by Lagrange D’Alembert methodologies and system control using a closed-loop PD controller. Innovative research in self-localization is presented in this thesis with the use of a double compass configuration that exploits a fusion of relative and absolute localization methods to achieve an analytical solution to position. In order to validate this novel double compass self-localization method, an optimized method was proposed in the form of an overhead computer system and a two-wheel manoeuvrable nonholonomic mobile robot was developed to facilitate research in self-localization methods with shaft encoders, accelerometers, magnetometers, and gyroscopes. The computer system was used to improve the performance of track non-natural markers on the mobile robot. A novel pseudo random algorithm with a gradient policy, inspired by the skip-list method, was delivered to significantly improve the image scanning performance to find non-natural markers. The validation, analysing the data collected from double compass configuration compared to visual tracking data was carried out using a non-parametric single-sample statistical analysis using the Kolmogorov-Smirnov test and the results validated the null hypothesis with a mean error less than 12mm. After solving the translational position of the mobile robot on a 2-dimentional plane, the mobile robot needs to be aware of its 3-dimentional orientation. To achieve this, a 9-axis sensor using an accelerometer, a gyroscope, and a magnetometer were implemented, to form an inertial measurement unit capable of returning a highly accurate self-orientation position using a directional cosine matrix which returns model free from accumulating error. A novel closed-loop PI controller was derived using the directional cosine matrix. In order to validate the directional cosine matrix method, data was collected from the sensor and compared against visual tracking data. The directional cosine matrix method data was validated using a non-parametric single-sample statistical analysis using the Kolmogorov-Smirnov test validated the null hypothesis with a mean error less than 1˚.
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