🤖 AI Summary
This work proposes a semantic navigation approach for mobile robots operating in human-centric environments by leveraging environmental semantic concepts and their interrelationships. The authors develop an ontology-driven semantic model of the environment and implement two distinct reasoning architectures—one based on relational databases and the other on KnowRob—integrating both into a unified semantic navigation system. For the first time, the study presents a systematic qualitative and quantitative comparison of these ontology-based reasoning frameworks at the system level. The feasibility and effectiveness of the proposed methods are validated through real-world experiments on a physical robot platform, establishing a reproducible and comparable benchmark framework for semantic navigation research.
📝 Abstract
Semantic navigation is the navigation paradigm in which environmental semantic concepts and their relationships are taken into account to plan the route of a mobile robot. This paradigm facilitates the interaction with humans and the understanding of human environments in terms of navigation goals and tasks. At the high level, a semantic navigation system requires two main components: a semantic representation of the environment, and a reasoner system. This paper is focused on develop a model of the environment using semantic concepts. This paper presents two solutions for the semantic navigation paradigm. Both systems implement an ontological model. Whilst the first one uses a relational database, the second one is based on KnowRob. Both systems have been integrated in a semantic navigator. We compare both systems at the qualitative and quantitative levels, and present an implementation on a mobile robot as a proof of concept.