Ll subcategories. Robot Ontology [15], SUMO [18], ADROn [30], and OASys [24] only model partial understanding for this category, neglecting all other categories. Concerning Environment Mapping, Space Ontology [8] models only the geographical info and absolutely nothing from all other categories. All other ontologies, but Robot Ontology [15], SUMO [18], ADROn [30], and OASys [24], partially represent this category. Only Core Ontology for Robots Automation (CORA) [10], POS [26], and ROSPlan [9] are focused around the two initial categories. Few on the revised ontologies partially model the information of Timely Details [11,12,17,19,28,29,34,36], also these analyzed ontologies partially model aspects in all categories.Robotics 2021, ten,5 ofConcerning Workspace Information and facts, some ontologies let representing certain domain objects, like the ontologies proposed in [22,25,31], which represent distinct objects of an workplace (e.g., monitor, desk, printer) to describe the robot’s environment; KnowRob [13] along with the ontology proposed by Hotz et al. in [23] let representing objects of restaurant environments, including cup, chair, and kitchen; as well as the one proposed by Sun et al. in [32] related to Search and Rescue (SAR) scenarios that model ideas which include search and rescue. The remain operates [16,21,27,336] are designed for any non-specific indoor environments with concepts including cabinet, sink, sofa, and beds. Table 1 shows that handful of ontologies contemplate Timely Details, therefore, the majority of them disregard dynamic environments for SLAM options; none of the ontologies analyzed, with all the exception of your proposed OntoSLAM, models all 13 aspects of SLAM know-how, presenting limitations to solve the SLAM dilemma. Though there exist quite a few ontologies to represent such knowledge, it’s evident that there’s a lack of a regular arrangement and generic ontology covering the complete aspects with the SLAM know-how. In this sense, OntoSLAM represents a novel development of an ontology, which is a worldwide resolution that covers each of the proposed subcategories. In distinct, it models the dynamics with the SLAM method by such as uncertainty of robot and landmarks positions. The following section explains the proposal in detail. three. OntoSLAM: The Proposal To be in a position of representing all understanding associated to SLAM and overcome the limitations of current ontologies, within this perform it is proposed OntoSLAM, an extensible and full SLAM ontology, freely accessible (https://github.com/Alex23013/ontoSLAM accessed on 16 November 2021). For the design and style of OntoSLAM, the following ontologies are made use of as a basis: ISRO [11]: it truly is a recent RP101988 MedChemExpress created ontology within the field of service robotics, GLPG-3221 Technical Information together with the aim of improving human-robot interactions; thus, it involves robotic and human agents in its models. The ontology proposed by V. Fortes [12]: It’s going to hereafter be referred as FR2013 ontology; it’s an ontology aimed at solving the issue of mixing maps when two robots collaboratively map a space; it integrates and extends POS [26] and CORA [10] ontologies (developed by the IEEE-RAS operating group) [15], which in turn inherit basic concepts in the SUMO ontology [18], that has been very referenced. KnowRob ontology [13]: it is a framework created for teleoperation environments, made about a robotic agent, whose most important mission should be to fetch issues and it ought to perform SLAM to fulfill this mission; as a result, the ontology enables describing the location exactly where it is actually; this ontology is already.
