A RAM-Based Neural Network for Collision Avoidance in a Mobile Robot

Q. Yao
Daryl G. Beetner, Missouri University of Science and Technology
Donald C. Wunsch, Missouri University of Science and Technology
B. Osterloh

This document has been relocated to http://scholarsmine.mst.edu/ele_comeng_facwork/821

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A RAM-based neural network is being developed for a mobile robot controlled by a simple microprocessor system. Conventional neural networks often require a powerful and sophisticated computer system. Training a multi-layer neural network requires repeated presentation of training data, which often results in very long learning time. The goal for this paper is to demonstrate that RAM-based neural networks are a suitable choice for embedded applications with few computational resources. This functionality is demonstrated in a simple robot powered by an 8051 microcontroller with 512 bytes of RAM. The RAM-based neural network allows the robot to detect and avoid obstacles in real time.