Two-Wheeled Self-Balancing Robot (TWSBR) has many applications and is an important research topic in robotics and control engineering. The principle behind the operation of a TWSBR is an inverted pendulum. The mathematical modelling of the TWSBR is still challenging as it includes non linear components to operate. This study aims the analysis of a two-wheeled, micro-controller based fabricated TWSBR. The linear mathematical model of each of the components of TWSBR has been obtained. Some of the components has been modelled using experimental model identification method while some are represented analytical approximations. A PID controller has been used to balance the TWSBR in upright position. The overall system has been simulated in MATLAB/SIMULINK. The result from the simulation is compared with real time hardware setup of the system to validate the mathematical model and stability of the TWSBR. The investigation has shown adequate accuracy in the models of components used in TWSBR. Moreover, the simulated results shows similar behaviour to that of hardware realization of the robot. This study may help on several research related to system identification, mathematical modeling and controller design of TWSBR.

Robot mapping and localization are essential components of mobile robotics, providing the foundation for path planning, navigation, and AR/VR applications. This project aims on utilizing a monocular camera as the sole sensor to construct a comprehensive map of an environment and achieve precise robot localization within it. Visual Simultaneous Localization and Mapping (VSLAM) techniques, leveraging affordable cameras, are employed to address challenges posed by dynamic environments, varying lighting conditions, and texture-less surfaces. The project aims to implement the Stella VSLAM algorithm for robust feature extraction, enabling accurate mapping and localization capabilities. For efficient communication and coordination among parallel processes, as well as seamless interaction between the robot and a PC, the project leverages the Robot Operating System (ROS). ROS serves as a reliable communication framework, optimizing inter-process communication and enhancing overall system performance. By offloading computationally intensive tasks to the PC, the project maximizes the robot's execution capabilities while receiving commands from the PC. The integration of the Stella VSLAM algorithm and ROS contributes to the development of a reliable and accurate mapping and localization system for various mobile robotics applications.