郭晏, 宋爱国, 包加桐, 崔建伟, 章华涛. 基于差分进化支持向量机的移动机器人可通过度预测[J]. 机器人, 2011, 33(3): 257-264,272.
引用本文: 郭晏, 宋爱国, 包加桐, 崔建伟, 章华涛. 基于差分进化支持向量机的移动机器人可通过度预测[J]. 机器人, 2011, 33(3): 257-264,272.
GUO Yan, SONG Aiguo, BAO Jiatong, CUI Jianwei, ZHANG Huatao. Mobile Robot Traversability Prediction Based on Differential Evolution Support Vector Machine[J]. ROBOT, 2011, 33(3): 257-264,272.
Citation: GUO Yan, SONG Aiguo, BAO Jiatong, CUI Jianwei, ZHANG Huatao. Mobile Robot Traversability Prediction Based on Differential Evolution Support Vector Machine[J]. ROBOT, 2011, 33(3): 257-264,272.

基于差分进化支持向量机的移动机器人可通过度预测

Mobile Robot Traversability Prediction Based on Differential Evolution Support Vector Machine

  • 摘要: 提出了一种移动机器人可通过度预测方法.给出了基于相对震动强度的可通度描述.通过提取典型地表图像的色彩和纹理特征并测量机器人通过该地表的相对震动强度建立训练样本集.使用差分进化算法优化支持向量机模型参数形成差分进化支持向量机对训练样本和相对震动强度进行拟合.在移动机器人运行过程中,线性分割前方地表图像形成预测子区域,通过提取各子区域内的色彩和纹理特征,利用训练好的差分进化支持向量机进行可通过度预测.考虑到移动机器人运动的柔顺性,给出了带有距离因子的基于可通过度预测值的最优路径方法.实验表明,该方法可以有效地预测复杂地表环境下的移动机器人可通过度.

     

    Abstract: A traversability prediction method for the mobile robot is presented.The traversability is described with the relative vibration.The color and texture features,which are one component of training sample,are extracted from the images of typical terrain.And the relative vibration intensity,which is the other component of training sample,is measured when the mobile robot travels across the typical terrain.The fitting method between training samples and relative vibration intensity based on differential evolution support vector machine is developed,of which the model parameters are optimized with differential evolution algorithm.When the mobile robot works,the image of the front terrain is linearly divided into sub-regions for prediction.The color and texture features of the sub-regions are extracted and the traversability values of the sub-regions are predicted based on the trained differential evolution support vector machine.For the smoothness of movement,the optimal path method is given based on traversability prediction with distance coefficient.The experiment demonstrates that the method can give an effective traversability prediction of the complex terrain for the mobile robot.

     

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