A robotic knowledge base to model and update real-world information from indoor environments

Robotic systems operating in the real world would benefit from a clear semantic model to understand their interactions with the real world. Such semantics are typically captured in an ontology. Unfortunately, the underlying model of existing ontologies requires many work-arounds before it can be used to capture general knowledge about objects and interactions in the real physical world. To remove such work-arounds, this article adopts the richer hypergraph model. It is used to develop an ontology, which is further implemented as the knowledge base of an actual robotic system performing search operations. Also, actual information extracted from the robot's sensors is used to update its knowledge base logically and sensibly.