Overview
Trained a client (Global Head of Conduct for a major investment bank) to implement a Multi-Layer Perceptron Neural Network for a self-driving robot as part of her MSc in Artificial Intelligence at the University of Essex.
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Objective
Program the robot to guide itself to follow a wall on its left, and turn around corners.
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Ethics
Client obtained permission from Course Director to obtain training for her programming skills.
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Results
Client received a mark of 70% for the project; robot demonstration was successful, and written report communicated that the client understood the underlying material.
Inputs
Distance from left sonar sensor to wall, distance from front sonar sensor to wall.​​
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Outputs
Left wheel speed, right wheel speed.
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Architecture
2-4-2 Topology (2 input layer nodes, 4 hidden layer nodes, 2 output layer nodes).
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Language
C++ (Neural Network), Matlab (Data Cleaning).
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Steps
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Client adapted a vanilla network to track and output the error (average RMS error after each training example) to determine when to stop training (minimised validation error over epochs).
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Also needed to perform data "de-duplication" and splitting into training, validation and test sets, and normalisation / de-normalisation of data.
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Client could have improved the smoothness by training over more epochs but the client was more than satisfied.
