On, A.E. and M.A.; writing–original draft preparation, M.A. and J.V.; writing–review and editing, A.E.; J.V., A.A.N. and E.A.; supervision, A.E.; visualization, M.A. in addition to a.E.; project administration, A.E.; funding acquisition, J.V. All authors have read and agreed to the published PSB-603 MedChemExpress version with the manuscript. Funding: This work was supported by Shahrekord University, and Jochem Verrelst was supported by the European Investigation Council (ERC) under the ERC-2017-STGSENTIFLEX project (grant agreement 755617). Conflicts of Interest: The authors declare no conflict of interest.
roboticsArticleOntoSLAM: An Ontology for Representing Location and Simultaneous Mapping Details for Autonomous RobotsMaria A. Cornejo-Lupa 1, , Yudith Cardinale 2,three, , , Regina Ticona-Herrera 1, , Dennis Barrios-Aranibar 2, , Manoel Andrade four and Jose Diaz-Amado two,3Computer Science Deparment, Universidad Cat ica San Pablo, Arequipa 04001, Peru; [email protected] (M.A.C.-L.); [email protected] (R.T.-H.) Electrical and Electronics Engineering Department, Universidad Cat ica San Pablo, Arequipa 04001, Peru; [email protected] (D.B.-A.); [email protected] (J.D.-A.) Department of Computer system Science, Universidad Sim Bol ar, Caracas 1086, Venezuela Instituto Federal da Bahia, Vitoria da Conquista 45078-300, Brazil; [email protected] Correspondence: [email protected] These authors contributed equally to this work.Citation: Cornejo-Lupa, M.A.; Cardinale, Y.; Ticona-Herrera, R.; Barrios-Aranibar, D.; Andrade, M.; Diaz-Amado, J. OntoSLAM: An Ontology for Representing Location and Simultaneous Mapping Data for Autonomous Robots. Robotics 2021, ten, 125. https:// doi.org/10.3390/robotics10040125 Academic Editor: Rui P. Rocha Received: 9 October 2021 Accepted: 15 November 2021 Published: 21 NovemberAbstract: Autonomous robots are playing an important function to resolve the Simultaneous Localization and Mapping (SLAM) trouble in distinct domains. To produce versatile, intelligent, and interoperable solutions for SLAM, it can be a need to to model the complicated understanding managed in these scenarios (i.e., robots qualities and capabilities, maps info, places of robots and landmarks, etc.) having a regular and formal representation. Some research have proposed ontologies as the common representation of such expertise; on the other hand, the majority of them only cover partial aspects on the facts managed by SLAM options. Within this context, the primary contribution of this function is often a comprehensive ontology, known as OntoSLAM, to model all elements connected to autonomous robots and the SLAM trouble, towards the standardization required in robotics, that is not reached until now using the current SLAM ontologies. A comparative evaluation of OntoSLAM with state-of-the-art SLAM ontologies is performed, to show how OntoSLAM covers the gaps from the existing SLAM information representation models. Benefits show the superiority of OntoSLAM at the Domain Understanding level and similarities with other ontologies at Lexical and Structural levels. Moreover, OntoSLAM is integrated into the Robot Operating Method (ROS) and Gazebo simulator to test it with Pepper robots and demonstrate its suitability, applicability, and flexibility. Experiments show how OntoSLAM offers GYKI 52466 Technical Information semantic added benefits to autonomous robots, including the capability of inferring information from organized know-how representation, with no compromising the details for the application and becoming closer for the standardization required.