Efficient object search for mobile robots in dynamic environments: Semantic map as an input for the decision maker

In this work we study the efficient search of objects in domestic environments, using probabilistic logic to represent uncertainty about object location and partially observable Markov decision processes (POMDP) for the decision-making process regarding the movements to be carried out by the robot to improve its belief about the object locations. We propose the use of a semantic map that stores information about the knowledge in the system and updates it, by an inference process, with sensor information received from the object recognition module. However, semantic maps are not capable of actively search for more information in the environment. For that reason a decision-making module, based on a POMDP framework, is integrated in the system. Several experiments were made in a realistic apartment test bed using every day objects and a mobile robot, showing that this hybrid solution makes the search process more efficient.