New Paper Published: Enhancing Underwater Robotics with Rule-Based Reasoning

I’m excited to share that our latest paper has just been published in Sensors! 🎉 We’ve developed a new rule-based reasoner for underwater robots, which uses the Web Ontology Language (OWL) and the Semantic Web Rule Language (SWRL). This approach really boosts the inference capabilities of ontology-based models, making underwater operations more efficient and accurate (Zhai et al., 2018).

Key Highlights:

Better Coordination: Our reasoner helps different underwater robots work together smoothly by ensuring they all understand the information in the same way.
Improved Inference: By integrating SWRL rules directly into OWL-based ontologies, we get better query results, which means smarter decisions during underwater missions.
Real-World Impact: This tech could be a game-changer for underwater exploration, environmental monitoring, and resource management, providing reliable autonomous operations.

On the left, connecting the SWARMs ontology and rule-based reasoner. Right, the rule-based reasoner based on Jena inference framework

SWARMs

References

2018

Sensors
A Rule-Based Reasoner for Underwater Robots Using OWL and SWRL

Zhaoyu Zhai, José-Fernán Martínez-Ortega, Néstor Lucas-Martínez, and Pedro Castillejo

Sensors, Oct 2018

Abs Bib DOI HTML PDF

Web Ontology Language (OWL) is designed to represent varied knowledge about things and the relationships of things. It is widely used to express complex models and address information heterogeneity of specific domains, such as underwater environments and robots. With the help of OWL, heterogeneous underwater robots are able to cooperate with each other by exchanging information with the same meaning and robot operators can organize the coordination easier. However, OWL has expressivity limitations on representing general rules, especially the statement “If … Then … Else …”. Fortunately, the Semantic Web Rule Language (SWRL) has strong rule representation capabilities. In this paper, we propose a rule-based reasoner for inferring and providing query services based on OWL and SWRL. SWRL rules are directly inserted into the ontologies by several steps of model transformations instead of using a specific editor. In the verification experiments, the SWRL rules were successfully and efficiently inserted into the OWL-based ontologies, obtaining completely correct query results. This rule-based reasoner is a promising approach to increase the inference capability of ontology-based models and it achieves significant contributions when semantic queries are done.
@article{zhai2018b, title = {A {{Rule-Based Reasoner}} for {{Underwater Robots Using OWL}} and {{SWRL}}}, author = {Zhai, Zhaoyu and {Mart{\'i}nez-Ortega}, Jos{\'e}-Fern{\'a}n and {Lucas-Mart{\'i}nez}, N{\'e}stor and Castillejo, Pedro}, year = {2018}, month = oct, date = {2018-10-16}, journal = {Sensors}, volume = {18}, number = {10}, pages = {3481}, issn = {1424-8220}, doi = {10.3390/s18103481}, urldate = {2024-08-11}, copyright = {CC BY 4.0}, langid = {english}, dimensions = {true}, google_scholar_id = {UeHWp8X0CEIC}, }

Key Highlights:

Related projects:

SWARMs

References

2018