In the absence of university facilities to physically construct and manufacture a prototype mini robot for testing due to Covid 19 restrictions, alternative methods have been introduced and adopted to replace the original aims and objectives that was initially proposed. This dissertation is predominately theory based due to the prevailing conditions. Autonomous automation, driverless cars is the next best opportunity on the horizon, certainly the next big thing for the future. Safety is the number 1 priority in this field, and it is imperative that machines, robots, driverless cars have the full complement of safety features to prevent injury and death. This paper explores various possibilities in the search for the best sensors for obstacle avoidance in robots. A strong literature review looks at what has been done in the recent past and what is currently being done now. Methodology sets out all the hardware and software alternatives by introducing a selection matrix to determine the best product for the prototype robot. It was found that a four-wheel driving platform is better than two wheeled platform as it was deemed more manoeuvrable; more room to add extra bits and pieces or sensors if need be. From the sensor selection matrix, 3 alternative but useful sensors were selected for trails on the robot. Ultrasonic, Camera and IR sensor (Infrared) respectively, then they were individually researched for further suitability, plus how they would work on the robot. Software and hardware platforms were selected for the job to support these sensors (some having their own selection matrix), to achieve optimum performance. The Raspberry Pi was chosen to be the microcontroller along with Python language software. MATLAB was introduced quite late on as an alternative to the physical build and python language as it was not envisaged at the time to run a simulation software package. MATLAB did not produce the results as expected due to the limited time frame, but an example format was used instead to show feasibility.
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MacDonald, John
Rights Holders: MacDonald, John Supervisors: Barker, Steve
School of Engineering, Computing and Mathematics
MSc Automotive Engineering
2020
© MacDonald, John Published by Oxford Brookes UniversityAll rights reserved