Object-oriented vision for a behavior-based robot

As one realization out of the class of behavior-based robot architectures a specific concept of situation-oriented behavior-based navigation has been proposed. Its main characteristic is that the selection of the behaviors to be executed in each moment is based on a continuous recognition and evaluation of the dynamically changing situation in which the robot is finding itself. An important prerequisite for such as approach is a timely and comprehensive perception of the robot's dynamically changing environment. Object-oriented vision as proposed and successfully applied, e.g., in freeway traffic scenes is a particularly well suited sensing modality for robot control. Our work concentrated on modeling the physical objects which are relevant for indoor navigation, i.e. walls, intersections of corridors, and landmarks. In the interest of efficiency these models include only those necessary features for allowing the robot to reliably recognize different situations in real time. According to the concept of object- oriented vision recognizing such objects is largely reduced to a knowledge-based verification of objects or features that may be expected to be visible in the current situation. The following results have been achieved: 1) By using its vision system and a knowledge base in the form of an attributed topological map the robot could orient itself and navigate autonomously in a known environment. 2) In an unknown environment the robot was able to build, by means of supervised learning, an attributed topological map as a basis for subsequent autonomous navigation. 3) The experiments could be performed both under unmodified artificial light and under natural light shining through the glass walls of the building.