Abstract: In this paper, the combination of machine learning & neural networking is described, a self-driving toy robot. The body of the robot is built with Lego Mindstorms. An Android smartphone is used to capture the view in front of the robot. A user first teaches the robot how to drive; this is done by making the robot go around a track a small number of times. The Image data, along with the user action is used to train a Neural Network. At run-time, Images of what is in front of the robot are fed into the neural network and the appropriate driving action is selected. The following vehicle will follow the target (i.e., Front) vehicle automatically. The other application is automated driving during the heavy traffic jam, hence relaxing driver from continuously pushing brake, accelerator or clutch. The Idea described in this paper has been taken from the Google car, defining the one aspect here under consideration is making the destination dynamic. This project showcases the poi of python’s libraries, as they enabled me to put together a sophisticated working system in a very short amount of time. Specifically, I made use of the Python Image Library to down sample Images, as Ill as the Pyran neural network library. The robot was controlled using the NXT-python library. This paper further Improves as near technologies Ire used as compared to previous projects, which significantly helps In Improving execution time & runtime memory.
Keywords: auto-driving; self-driving; neural networks; robotics; machine learning; supervised learning; artificial intelligence; python.
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DOI:
10.17148/IJARCCE.2021.10206
