Robot Semantic Protocol (RoboSemProc) for Semantic Environment Description and Human–Robot Communication

In the last decades, the focus has shifted towards mobile robots to link predictions, imagination, and expectations to human life in different aspects. A tremendous amount of research on mobile robots indicates their importance in various industrial and non-industrial fields such as production, medicine and agriculture. Despite all of these innovations in the field of robotics, intelligent mobile robots are facing challenges in processing the vast amount of sensory data from their sensory inputs. Due to the increasing amount of sensory data, a new demand is to process sensory inputs in an understandable form for both humans and robots. One approach to processing sensory data in a way that is understandable for both robots and humans is through the use of semantic technology, which is a major technology for building semantic knowledge bases in a machine-readable form. The success of semantic technology is highly reliant on ontologies which are considered the semantic knowledge representation. The huge amount of research in this field proves the undeniable impact of ontologies in the field of robotics. Yet, the work concerning the conversion of the sensory inputs from a mobile robot into semantic information in real-time are scarce. This transformation becomes more challenging when converting the sensory input of multiple sensors to a single semantic statement. The collection of semantic information for real-time ontology population is another challenge. In this regard, there is also a lack of work in transferring and using this information for natural language communication between humans and robots. This work addresses these challenges and employs semantic technology in the field of robotics to enable a mobile robot to create semantic information during exploration. In addition, the resulted semantic information is used for communicating information as well as facilitating communication between other robots and humans in a natural language.

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