Camera-based observation of obstacle motions to derive statistical data for mobile robot motion planning

Abstract

Mobile robots for advanced applications have to act in environments which contain moving obstacles. Motions of obstacles (e.g. humans) usually are not precisely predictable, but neither they are completely random. Long-term observation of obstacle behavior may yield knowledge about prevailing motion patterns. This paper presents concepts and results of an experimental system. Using cameras mounted at the ceiling, the workspace is observed. The image data is processed and transformed into a compact statistical representation of motion patterns. Mobile robot motion planning benefits from such additional knowledge: trajectories are rated in terms of collision probability and expected time for reaching the goal; planning yields efficient paths which are adapted to obstacle behavior.