Searching objects in large-scale indoor environments: A decision-theoretic approach

Many of today´s mobile robots are supposed to perform everyday manipulation tasks autonomously. However, in large-scale environments, a task-related object might be out of the robot´s reach. Hence, the robot first has to search for the object in its environment before it can perform the task. In this paper, we present a decision-theoretic approach for searching objects in large-scale environments using probabilistic environment models and utilities associated with object locations. We demonstrate the feasibility of our approach by integrating it into a robot system and by conducting experiments where the robot is supposed to search different objects with various strategies in the context of fetch-and-delivery tasks within a multi-level building.