Neural Network Based Autonomous Navigation
Neural Network Based Autonomous Navigation
复制标题
基于神经网络的自主导航
DOI：
10.1007/978-1-4613-1533-9_5
复制
发表时间：
1990
期刊：
影响因子：
--
通讯作者：
D. Pomerleau
中科院分区：
文献类型：
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作者：
D. Pomerleau
对于传统的视觉和机器人技术来说，自主导航一直是一个难题，主要是因为与现实世界场景相关的噪声和可变性。基于传统图像处理和模式识别技术的自主导航系统在某些条件下往往表现良好，但在其他条件下却存在问题。部分困难源于这样一个事实，即这些系统执行的处理在不同的驾驶情况下保持固定。人工神经网络在其他具有高度噪声和可变性的领域显示出良好的性能和灵活性。示例包括手写字符识别[3][5]和语音识别[10]。ALVINN(神经网络中的自主陆地车辆)旨在将连接主义技术的灵活性引入到自主导航任务中。具体地说，ALVINN是一个人工神经网络，旨在驱动卡内基梅隆大学的自动导航测试车辆NavLab。
Autonomous navigation has been a difficult problem for traditional vision and robotic techniques, primarily because of the noise and variability associated with real world scenes. Autonomous navigation systems based on traditional image processing and pattern recognition techniques often perform well under certain conditions but have problems with others. Part of the difficulty stems from the fact that the processing performed by these systems remains fixed across various driving situations.Artificial neural networks have displayed promising performance and flexibility in other domains characterized by high degrees of noise and variability. Examples include handwritten character recognition [3][5] and speech recognition [10]. ALVINN (Autonomous Land Vehicle In a Neural Network) is designed to bring the flexibility of connectionist techniques to the task of autonomous navigation. Specifically, ALVINN is an artificial neural network designed to drive the Navlab, the Carnegie Mellon autonomous navigation test vehicle.