This paper describes the actions taken in developing a framework that aims to improve the motion planning
of a manipulative robotic agent through reasoning based on semantic knowledge. The Semantic Web Rule
Language (SWRL) was employed to draw new insights from the existing information about the robotic
system and its environment. Recent ontology-based standards have been developed (IEEE 1872-2015;
IEEE 1872.2-2021; IEEE 7007-2021), and others are currently under development (IEEE P1872.1; IEEE
P1872.3) to improve robot performance in task execution. Ontological knowledge “semantic map" was
generated using a deep neural network trained to detect and classify objects in the environment where the
manipulator agent acts. Manipulation constraints were deduced, and the environment corresponding to the
agent’s manipulation workspace was created so the planner could interpret it to generate a collision-free
path. Several SPARQL queries were used to explore the semantic map and allow ontological reasoning.
The proposed framework was implemented and validated in a real experimental setting, using the ROSPlan
planning framework to perform the planning tasks. This ontology-based framework proved to be a
promising strategy. E.g., it allows the robotic manipulative agent to interact with objects, e.g., to choose a
mobile phone or a water bottle, using semantic information from the environment to solve the requested
tasks.This work is financed by national funds through FCT - Foundation for Science and Technology,
I.P., through IDMEC, under LAETA, project UIDB/50022/2020. The work of Rodrigo Bernardo
was supported by the PhD Scholarship BD/6841/2020 from FCT. This work has received funding
from: the project 0770_EUROAGE2_4_E (POCTEP Programa Interreg V-A Spain-Portugal),
and the European Union’s Horizon 2020 programme under StandICT.eu 2023 (under Grant
Agreement No.: 951972).info:eu-repo/semantics/publishedVersio
