文献类型：会议论文

英文题名：Research of Terrain Recognition for Off-road Robot Based on Extreme Learning Theory

作者：Liu, Yanxia[1];Fang, Jianjun[1];Liu, Caixia[1]

第一作者：刘艳霞

通讯作者：Liu, YX[1]

机构：[1]Beijing Union Univ, Coll Automat, Beijing, Peoples R China

第一机构：北京联合大学城市轨道交通与物流学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Automat, Beijing, Peoples R China.|[1141751]北京联合大学城市轨道交通与物流学院;[11417]北京联合大学;

会议论文集：International Conference on Advanced Mechatronic Systems (ICAMechS)

会议日期：NOV 30-DEC 03, 2016

会议地点：Melbourne, AUSTRALIA

语种：英文

外文关键词：Terrain Recognition; Extreme learning algorithm; Texture feature extraction; Robot

摘要：Feature extraction and classification algorithm is the key to classification accuracy. Terrain recognition for off road robot need higher real-time classification algorithm, while the traditional neural network training method is difficult to meet the requirements. Extreme learning machine is used to classify the terrain pictures collected by robot in real time. Experimental results show that the accuracy of ELM terrain classification is slightly higher than the traditional neural network algorithm, but algorithm efficiency is raised more than a dozen times for the small sample size of 150, which meets the requirements for accuracy, especially for real time.