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DESIGN AND IMPLEMENTATION OF SENSING METHODS ON ONE-TENTH SCALE OF AN AUTONOMOUS RACE CAR

thesis
posted on 02.12.2020, 15:42 by Harshitha Veeramachaneni

Self-driving is simply the capacity of a vehicle to drive itself without human intervention. To accomplish this, the vehicle utilizes mechanical and electronic parts, sensors, actuators and an AI computer. The on-board PC runs advanced programming, which permits the vehicle to see and comprehend its current circumstance dependent on sensor input, limit itself in that climate and plan the ideal course from point A to point B. Independent driving is not an easy task, and to create self-sufficient driving arrangements is an exceptionally significant ability in the present programming designing field.

ROS is a robust and versatile communication middle ware (framework) tailored and widely used for robotics applications. This thesis work intends to show how ROS could be used to create independent driving programming by investigating self-governing driving issues, looking at existing arrangements and building up a model vehicle utilizing ROS.

The main focus of this thesis is to develop and implement a one-tenth scale of an autonomous RACECAR equipped with Jetson Nano board as the on-board computer, PCA9685 as PWM driver, sensors, and a ROS based software architecture.

Finally, by following the methods presented in this thesis, it is conceivable to build an autonomous RACECAR that runs on ROS.

By following the means portrayed in this theory of work, it is conceivable to build up a self-governing vehicle.


Funding

I was awarded with a scholarship of $4000 per semester by ECE department of IUPUI

History

Degree Type

Master of Science in Electrical and Computer Engineering

Department

Electrical and Computer Engineering

Campus location

Indianapolis

Advisor/Supervisor/Committee Chair

Dr. Lingxi Li

Additional Committee Member 2

Dr. Yaobin Chen

Additional Committee Member 3

Dr. Mohamed El-Sharkawy

Additional Committee Member 4

Dr. Brian King

Licence

Exports