Classification of obstacle shape for generating walking path of humanoid robot

Abstract

To generate the walking path of a humanoid robot in an unknown environment, the shapes of obstacles around the robot should be detected accurately. However, doing so incurs a very large computational cost. Therefore this study proposes a method to classify the obstacle shape into three types: a shape small enough for the robot to go over, a shape planar enough for the robot foot to make contact with, and an uncertain shape that must be avoided by the robot. To classify the obstacle shape, first, the range and the number of the obstacles is detected. If an obstacle can make contact with the robot foot, the shape of an obstacle is accurately derived. If an obstacle has uncertain shape or small size, the shape of an obstacle is not detected to minimize the computational load. Experimental results show that the proposed algorithm efficiently classifies the shapes of obstacles around the robot in real time with low computational load.