Qualitative Driving on Body-Centered Representation of Environment

Abstract

Mobile robots are expected to run in environments where unexpected scenes or obstacles may occur which disturb the exact measurement of distances in the space. In such an environment, it is difficult for a robot to move tactfully, avoiding obstacles e.g.walls, columns, etc. In order to achieve such tactful movements, it is desired that a mobile robot can focus attention to meaningful points around itself, and plan its own movement toward the goal. A key point for such an intelligent recognition-based control is to represent the meaning of the environment for the robot, i.e., whether visible objects help or disturb the robot's movement. In this paper, we present a qualitative reasoning based driving, for a mobile robot to park in an unknown, small square parking lot. The environment, observed by the omni-directional vision of the robot, is segmented according to the motion capability and shape of the robot, to generate a robot-centered logical description of the environment. In experiments, we obtained accurate, safe and efficient parking behaviors due to this description, in various simulated parking lots.