Spiking神经网络及其在移动机器人中的应用

本文介绍了第三代神经网络——Spiking神经网络的产生、特点、编码,Spiking神经元和神经突触模型,Spiking神经网络的训练方法等内容。并对国内外Spiking神经网络的研究动态和发展趋势、Spiking神经网络在移动机器人中的应用等内容进行了综述。Spiking神经网络具有其独有的特点:Spiking神经元同时包含有时空信息、很强的计算能力、较好的鲁棒性、易于硬件实现等。这些特点使得Spiking神经网络在移动机器人控制、环境感知和机器人视觉等领域得以成功应用。国家自然科学基金资助,项目批准号:60974603,61005009|河北省自然科学基金资助,项目批准号:F2008000197,F2010000437|河北师大重点基金,项目批准号:L2010Z08|河北师大博士基金资助,项目批准号:L2007B2

Corridor scene recognition for mobile robots based on multi-sonar-sensor information and NeuCube

为提高室内移动机器人的环境感知能力,针对其常处的结构化走廊场景的分类、Spiking神经网络(SNN)和基于SNN的新型计算模型NeuCube进 行研究。SNN利用尖脉冲传递时、空信息,比传统的神经网络更适于动态、时序信息的分析,以及各种模式信息的识别和分类。此外,SNN更易于用硬件实现。 在对NeuCube的基本原理、学习方法和计算步骤进行讨论的基础上,利用多超声传感信息和NeuCube对室内移动机器人常处的7种走廊场景进行识别。 实验结果表明基于多超声传感信息和NeuCube的移动机器人走廊场景分类方法可以对7种走廊场景进行有效识别,该方法有助于增强移动机器人的自主性和提 高其智能水平。To improve the perception ability of indoor mobile robots, the classification method for the commonly structured corridor-scenes, Spiking Neural Network (SNN) and NeuCube, which is a novel computing model based on SNN, were studied. SNN can convey spatio-temporal information by spikes. Besides, SNN is more suitable for analyzing dynamic and time-series data, and for recognizing data of various patterns than traditional Neural Network (NN). It is easy to be implemented by hardware. The principle, learning methods and calculation steps of NeuCube were discussed. Then seven common corridor scenes were recognized by the classification method based on multi-sonar-sensor information and NeuCube. The experimental results show that the proposed method is effective. Additionally, it is helpful for improving autonomy and intelligence of mobile robots