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Creating a standard for knowledge representation and reasoning in autonomous robotics is an urgent task if we consider recent advances in robotics as well as predictions about the insertion of robots in human daily life. Indeed, this will impact the way information is exchanged between multiple robots or between robots and humans and how they can all understand it without ambiguity. Indeed, Human Robot Interaction (HRI) represents the interaction of at least two cognition models (Human and Robot). Such interaction informs task composition, task assignment, communication, cooperation and coordination in a dynamic environment, requiring a flexible representation. Hence, this paper presents the IEEE RAS Autonomous Robotics (AuR) Study Group, which is a spin-off of the IEEE Ontologies for Robotics and Automation (ORA) Working Group, and its ongoing work to develop the first IEEE-RAS ontology standard for autonomous robotics. In particular, this paper reports on the current version of the ontology for autonomous robotics as well as on its first implementation successfully validated for a human-robot interaction scenario, demonstrating the developed ontology’s strengths which include semantic interoperability and capability to relate ontologies from different fields for knowledge sharing and interactions.

“This is a post-peer-review, pre-copyedit version of an article published in Journal of Intelligent & Robotic Systems. The final authenticated version is available online at: https://doi.org/10.1007/s10846-019-01107-w”.

An overview of the 1872-205 IEEE standard and the efforts related to it.

This is a post-peer-review, pre-copyedit version of an article published in Journal of Ambient Intelligence and Humanized Computing. The final authenticated version is available online at: https://doi.org/10.1007/s12652-020-02150-4