A Self-Driving Robot Using Deep Convolutional Neural Networks on Neuromorphic Hardware
| Autor: | Jeffrey L. Krichmar, Jacob Isbell, Nicolas Oros, Tiffany Hwu |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
FOS: Computer and information sciences
Multi-core processor Computer science business.industry Computer Science - Neural and Evolutionary Computing Robotics 02 engineering and technology Communications system Convolutional neural network TrueNorth Computer Science - Robotics Neuromorphic engineering 020204 information systems 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Artificial intelligence Neural and Evolutionary Computing (cs.NE) IBM business Robotics (cs.RO) Computer hardware |
| Zdroj: | IJCNN |
| DOI: | 10.48550/arxiv.1611.01235 |
| Popis: | Neuromorphic computing is a promising solution for reducing the size, weight and power of mobile embedded systems. In this paper, we introduce a realization of such a system by creating the first closed-loop battery-powered communication system between an IBM TrueNorth NS1e and an autonomous Android-Based Robotics platform. Using this system, we constructed a dataset of path following behavior by manually driving the Android-Based robot along steep mountain trails and recording video frames from the camera mounted on the robot along with the corresponding motor commands. We used this dataset to train a deep convolutional neural network implemented on the TrueNorth NS1e. The NS1e, which was mounted on the robot and powered by the robot's battery, resulted in a self-driving robot that could successfully traverse a steep mountain path in real time. To our knowledge, this represents the first time the TrueNorth NS1e neuromorphic chip has been embedded on a mobile platform under closed-loop control. Comment: 6 pages, 8 figures |
| Databáze: | OpenAIRE |
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