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               REFERENCES

               1.  Burke JL, Murphy RR, Rogers E, Lumelsky VJ, Scholtz J.  Final report for the DARPA/NSF interdisciplinary study on human­
                  robot interaction. IEEE Trans Syst , Man, Cybern C 2004;34:103–12.
               2.  Thrun S. Toward a framework for human­robot interaction. Human–Computer Interaction 2004;19:9–24. [DOI: 10.5898/JHRI.3.2.Beer]
               3.  Olszewska JI, Barreto M, Bermejo­Alonso J, et al. Ontology for autonomous robotics. In: 2017 26th IEEE
                  International Symposium on Robot and Human Interactive Communication (RO­MAN). IEEE; 2017. pp. 189–94.
               4.  de Freitas EP, Olszewska JI, Carbonera JL, et al. Ontological concepts for information sharing in cloud robotics. J Ambient Intell Human
                  Comput 2020:1–12.
               5.  Kostavelis I, Gasteratos A. Semantic mapping for mobile robotics tasks: A survey. Robotics and Autonomous Systems 2015;66:86–103.
               6.  Hanheide M, Göbelbecker M, Horn GS, et al. Robot task planning and explanation in open and uncertain worlds.
                  Artificial Intelligence 2017;247:119–50.
               7.  Toscano C, Arrais R, Veiga G. Enhancement of industrial logistic systems with semantic 3D representations for mobile manipulators. In:
                  Iberian Robotics conference. Cham: Springer International Publishing; 2018. pp. 617-28.
               8.  Olivares­Alarcos A, Beßler D, Khamis A, Goncalves P, Habib MK, et al. A review and comparison of ontology­based approaches to robot
                  autonomy. The Knowledge Engineering Review 2019;34.
               9.  Guarino N. Formal ontology in information systems: Proceedings of the first international conference (FOIS’98), June 6­8, Trento, Italy.
                  vol. 46. IOS press; 1998.
               10. Niles I, Pease A. Towards a standard upper ontology. In: Proceedings of the international conference on Formal Ontology in Information
                  Systems­Volume 2001; 2001. pp. 2–9.
               11. Lenat D, Guha R. Building large knowledge­based systems: Representation and inference in the CYC project. Artificial Intelligence
                  1993;61:53-63.
               12. Arp R, Smith B, Spear AD.  Building ontologies with basic formal ontology.  Mit Press; 2015.
               13. Masolo C, Borgo S, Gangemi A, Guarino N, Oltramari A. Wonderweb deliverable d18: Ontology library. Technical report, ISTC­CNR;
                  2003.
               14. Prestes E, Carbonera JL, Rama Fiorini S, et al. Towards a core ontology for robotics and automation. Robotics and Autonomous Systems
                  2013;61:1193–204.
               15. Balakirsky S, Schlenoff C, Rama Fiorini S, et al. Towards a robot task ontology standard. In: International
                  Manufacturing Science and Engineering Conference. vol. 50749. American Society of Mechanical Engineers; 2017. p. V003T04A049.
               16. Efficient integration of metric and topological maps for directed exploration of unknown environments. Robotics and Autonomous Systems
                  2002;41:21–39.
               17. Sim R, Little JJ. Autonomous vision­based robotic exploration and mapping using hybrid maps and particle filters. Image and Vision
                  Computing 2009;27:167–77.
               18. He Z, Sun H, Hou J, Ha Y, Schwertfeger S. Hierarchical topometric representation of 3D robotic maps. Auton Robot 2021;45:755-71.
               19. Niloy A, Shama A, Chakrabortty RK, et al. Critical design and control issues of indoor autonomous mobile robots: A Review. IEEE
                  Access 2021;9:35338-70.
               20. Liu J, Li Y, Tian X, Sangaiah AK, Wang J. Towards semantic sensor data: an ontology approach. Sensors (Basel) 2019;19:1193.
               21. Günther M, Wiemann T, Albrecht S, Hertzberg J. Model­based furniture recognition for building semantic object maps. Artificial Intelli
                  gence 2017;247:336–51.
               22. Lim GH, Suh IH, Suh H. Ontology­based unified robot knowledge for service robots in indoor environments. IEEE Trans Syst , Man,
                       Cybern A 2011;41:492-509.
               23. Rusu RB, Marton ZC, Blodow N, Holzbach A, Beetz M. Model­based and learned semantic object labeling in 3D point cloud maps of
                  kitchen environments. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE; 2009. pp. 3601–8.
               24. Galindo C, Saffiotti A. Inferring robot goals from violations of semantic knowledge. Robotics and Autonomous Systems 2013;61:1131–43.
               25. Wang T, Chen Q. Object semantic map representation for indoor mobile robots. In: Proceedings 2011 International Conference on System
                  Science and Engineering. IEEE; 2011. pp. 309–13.
               26. Vasudevan S, Siegwart R. Bayesian space conceptualization and place classification for semantic maps in mobile robotics. Robotics and
                  Autonomous Systems 2008;56:522–37.
               27. Diab M, Pomarlan M, Beßler D, et al. An ontology for failure interpretation in automated planning and execution. In:
                  Iberian Robotics conference. Springer; 2019. pp. 381–90.
               28. Balakirsky S. Ontology based action planning and verification for agile manufacturing. Robotics and Computer Integrated Manufacturing
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