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Self-organization of environmental maps based on scene images and navigation of mobile robots

基于场景图像的环境地图自组织及移动机器人导航

基本信息

批准号：
11680393
负责人：
ISHIKAWA Masumi
金额：
$ 2.37万
依托单位：
Kyushu Institute of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1999
资助国家：
日本
起止时间：
1999 至 2001
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-11680393/
关键词：
robots navigation self-organization omni-directional mirror environmental map scene images

项目摘要

1) Local autocorrelation functions proposed by otsu is appropriate for compressing scene images. We clarified that special resolution of 1/64 is the best based on the criterion of minimizing location estimation error.2) In constructing environmental maps by self-organization, inputs to SOM are made by adding location information to local autocorrelation functions of each scene image. Location information of a robot is corrupted by noise. In contrast to conventional Bayesian estimation, prior probability distribution is assumed to be Gaussian. A relative weight of location information is determined by minimizing location estimation error.3) Real robot is examined in a field including obstacles made of corrugated cardboard. As the robot proceeds, location and direction errors increase due to slipping of wheels, which is experimentally measured. As mentioned above, location information including error is added to local autocorrelation functions and is used as an extended input to SOM.4) When a new input vector is given, Bayesian estimation is effective in estimating location using constructed environmental maps. In contrast to conventional Bayesian estimation, prior probability at some time is a translation of a posterior probability at previous time due to movement of a robot.5) Since an environmental map does not include the location of obstacles, it is difficult to combine location estimation and obstacle avoidance. We propose to represent the distance between an infrared sensor and the an obstacle in the form of probability distribution, and demonstrate its effectiveness by simulation experiments.

1)大津提出的局部自相关函数适用于场景图像压缩。基于位置估计误差最小化的准则，我们阐明了1/64的空间分辨率是最佳的。2）在自组织环境地图构建中，通过将位置信息添加到每个场景图像的局部自相关函数中来向SOM输入。机器人的位置信息被噪声破坏。与传统的贝叶斯估计相比，先验概率分布被假定为高斯分布。通过最小化位置估计误差来确定位置信息的相对权重。3）对真实的机器人在包含由瓦楞纸板制成的障碍物的场地中进行检测。随着机器人的前进，位置和方向误差增加，由于打滑的车轮，这是实验测量。如上所述，包括误差的位置信息被添加到局部自相关函数中，并用作SOM的扩展输入。4）当给定新的输入向量时，贝叶斯估计在使用构造的环境地图估计位置中是有效的。与传统的贝叶斯估计相反，由于机器人的移动，在某个时间的先验概率是在先前时间的后验概率的平移。5）由于环境地图不包括障碍物的位置，因此难以将联合收割机位置估计和障碍物回避相结合。提出了用概率分布的形式表示红外传感器与障碍物之间的距离，并通过仿真实验验证了其有效性。