同時自己位置地図獲得手法を用いた自律型ロボットの環境認識

ダイナミカル局所ボロノイ分割による逐次経路計画

抄録

The Goal of this work is to provide a more in depth understanding of the navigation in the autonomous robot using stable visual points derived from the repeated experimentation by the stereo vision in a natural featured environment. In order to identify the position of the robot as well as to establish the 3 D obstacle map under the unknown environment, we discuss the simultaneous stereo type localization and map building (SLAM) problem. The design of the planning algorithm for a vision guided mobile robot depends upon the two main characteristics of visual environmental recognition i.e. Uncertainty and Efficiency. The uncertainty is reduced by the Extended Kalman Filter algorithm based on the process and observation model of the mobile robot. Regarding the efficiency, the optimal path planning algorithm which uses the dynamical localized Voronoi division is a new concept in our proposal. This method has the ability to make the path for mobile robot with only suitable number of natural features.