This research investigates the benefits of using a trajectory tracking controller based on a dynamic model for a four-Mecanum-wheeled vehicle (FMWV) over a kinematic-model-based controller. An FMWV was designed and built, incorporating both hardware and software components. Two Linear Quadratic Regulators (LQRs) based on kinematic and dynamic models were implemented. The dynamic model includes friction estimation, while the kinematic model assumes a no-slip condition. Simulation results indicate that the dynamic model reduces overshoot and improves trajectory tracking in low-friction scenarios compared to the kinematic model. High-friction scenarios show comparable performance for both controllers. Experimental results align with simulations, though some deviations highlight areas for further improvement. Overall, the dynamic-model-based controller demonstrates superior performance in low-friction conditions, reducing translational root mean square error (RMSE) and maximum path deviation (MaxE).

The amount of people dealing with dementia is rising globally. The amount of caretakers is, however, not. Therefore, technological aids are needed to support people dealing with dementia and relieve the stress on their caretakers. Current solutions provide tracking of people with dementia. Also, different robots exist that provide people with companionship. However, no solution exists that combines tracking and companionship capabilities. Therefore, the Smart Teddy is introduced. The Smart Teddy can track different indicators that indicate the progress of dementia and simultaneously provide the user with companionship through interaction. The goal of this thesis is to design a data acquisition system that acquires meaningful data that can be used for the development of algorithms that will autonomously determine the progress of dementia. To achieve this, a system with a Teddy and a Base station has been designed. The Teddy has a sound-, a carbon monoxide-, a smoke- and a movement sensor. Also, a real-time module is implemented to be able to assign the current time to the measurement data. Lastly, a GPS and GSM module is implemented to be able to track seniors in case they wander. In the Base station, a mmWave sensor is implemented that tracks the position, velocity, and direction of the persons present in the room. Also, a processor is implemented that gathers and stores the data from the mmWave sensor and the data from the Teddy which is sent via a LoRa connection. In addition, the designed system can store the collected data for more than one week. The collected data can be used by an expert in dementia to extract meaningful information about dementia progress, after that, an expert in digital signal processing is needed to develop algorithms that estimate the quality of life of a senior suffering from dementia.