Sensor-based obstacle modeling in configuration space for manipulator motion planning

Abstract

Presents an approach to sensor-based obstacle modeling in a configuration space for manipulator motion planning in unknown environments. In order to achieve this objective, an efficient algorithm is used to fast map obstacles based on defined fundamental obstacles in the workspace and their images in the configuration space. A robotic manipulator is assumed to be equipped with distance sensors to detect obstacles in the local region. By computation of the critical points of an obstacle based on information acquired by the distance sensors, an obstacle model in the configuration space is constructed. By using this sensor-based configuration space modeling, robot motion planning in unknown environments can be performed in realistic time frames.