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.
Q. Yao et al., "A RAM-Based Neural Network for Collision Avoidance in a Mobile Robot," Proceedings of the International Joint Conference on Neural Networks (2003, Portland, OR), Institute of Electrical and Electronics Engineers (IEEE), Jul 2003.
The definitive version is available at https://doi.org/10.1109/IJCNN.2003.1224077
International Joint Conference on Neural Networks, 2003 (2003: Jul. 20-24, Portland, OR)
Electrical and Computer Engineering
Keywords and Phrases
8051 Microcontroller; RAM-Based Neural Network; Collision Avoidance; Embedded Applications; Microprocessor System; Mobile Robots; Multi-Layer Neural Network Training; Neurocontrollers; Random-Access Storage
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Article - Conference proceedings
© 2003 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jul 2003