Y is evaluated with unique metrics, they’re assessed separately. Figure six shows subcategories of Functional Adequacy, in which OntoSLAM is equal or superior to its predecessors. In particular, OntoSLAM overcomes for more than 22 its predecessors in the sub-characteristic of Knowledge Reuse; it means OntoSLAM could be reused to further specialize the usage of ontologies within the field of robotics and SLAM. Moreover, the 3 ontologies exceed 50 inside the Functional Adequacy category. The evaluation on Compatibility, Operability, and Transferability categories is shown in Figure 7. Like inside the Functional Adequacy category, OntoSLAM is superior to its predecessors. Moreover, in these characteristics the 3 evaluated ontologies present behaviors above 80 . The highest score (97 ) was obtained by OntoSLAM inside the Operability category, which guarantees that OntoSLAM is usually effortlessly discovered by new customers.Figure six. Top quality Model: Functional Adequacy.Figure 7. Good quality Model: Operability, Transferability, Maintainability.Benefits of the Maintainability category are shown in Figure eight. When once more, OntoSLAM shows the most effective Icosabutate Icosabutate Biological Activity overall performance. Furthermore, the evaluated ontologies show the very best results, reaching 100 in some sub-characteristics, for instance Modularity and Modification Stability. Outcomes are above 80 on average for this category, which reveals that all of the ontologies evaluated are maintainable.Robotics 2021, 10,13 ofFigure 8. High quality Model: Maintainability.All these benefits from the OQuaRE metrics, demonstrate that the High-quality at Lexical and Structural levels of OntoSLAM is equivalent or slightly superior compared with its predecessor ontologies. four.2. Applying OntoSLAM in ROS: Case of Study To empirically evaluate and demonstrate the suitability of OntoSLAM, it was incorporated into ROS and also a set of experiments with simulated robots had been performed. The simulated scenarios and their validation are developed into 4 phases, as shown in Figure 9. The situation consists of two robots: Robot “A” executes a SLAM algorithm, by collecting environment details through its sensors and generates ontology situations, which are stored and GLPG-3221 Cancer published around the OntoSLAM web repository, and Robot “B” performs queries on the internet repository, thus, it really is able to obtain the semantic facts published by Robot “A” and use it for its wants (e.g., continue the SLAM method, navigate). The simulation is as follows:Figure 9. Data flow for the case of study.four.two.1. Data Gathering This phase offers together with the collection of the information to perform SLAM (robot and map info). For this objective, the well-known ROS plus the simulator Gazebo are used. The Pepper robot is simulated in Gazebo and scripts subscribed to the ROS nodes, fed by the internal sensors of Robot “A” are generated. With this details obtained in genuine time, it is actually possible to move on towards the transformation phase. four.2.2. Transformation This phase bargains with all the transformation with the raw information taken in the Robot “A” sensors to situations within the ontology (publish the data within the semantic repository) and theRobotics 2021, 10,14 oftransformation of instances in the ontology to SLAM data for Robot “B” or precisely the same Robot “A”, through the mapping approach or in another time. To complete so, the following functions are implemented: F1 SlamToOntology: to convert the raw data collected by the robot’s sensors inside the earlier phase into situations of OntoSLAM. Information for example the name with the robot, its position, plus the time.
