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A Self-Driving Robot Using Deep Convolutional Neural Networks on Neuromorphic Hardware

机译:基于maTLaB的深度卷积神经网络自驱动机器人   神经形态硬件

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摘要

Neuromorphic computing is a promising solution for reducing the size, weightand power of mobile embedded systems. In this paper, we introduce a realizationof such a system by creating the first closed-loop battery-poweredcommunication system between an IBM TrueNorth NS1e and an autonomousAndroid-Based Robotics platform. Using this system, we constructed a dataset ofpath following behavior by manually driving the Android-Based robot along steepmountain trails and recording video frames from the camera mounted on the robotalong with the corresponding motor commands. We used this dataset to train adeep convolutional neural network implemented on the TrueNorth NS1e. The NS1e,which was mounted on the robot and powered by the robot's battery, resulted ina self-driving robot that could successfully traverse a steep mountain path inreal time. To our knowledge, this represents the first time the TrueNorth NS1eneuromorphic chip has been embedded on a mobile platform under closed-loopcontrol.
机译:神经形态计算是减少移动嵌入式系统的尺寸,重量和功能的一种有前途的解决方案。在本文中,我们通过在IBM TrueNorth NS1e和基于Android的自主机器人平台之间创建第一个闭环电池供电的通信系统,介绍了这种系统的实现。使用此系统,我们通过沿着陡峭的山径手动驱动基于Android的机器人并记录安装在机器人上的摄像机的视频帧以及相应的电机命令,构建了路径遵循行为的数据集。我们使用此数据集来训练在TrueNorth NS1e上实现的深层卷积神经网络。安装在机器人上并由机器人电池供电的NS1e导致了自动驾驶机器人,该机器人可以成功地实时穿越陡峭的山路。据我们所知,这是TrueNorth NS1eneuromorphic芯片首次在闭环控制下嵌入移动平台。

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