Reasoning Systems for Semantic Navigation in Mobile Robots

Jonathan Crespo; Ramón Barber; O. M. Mozos; Daniel Beßler; Michael Beetz

cs.ROMar 23, 2026

Reasoning Systems for Semantic Navigation in Mobile Robots

Authors: Jonathan Crespo, Ramón Barber, O. M. Mozos, Daniel Beßler, Michael Beetz

Organizations: University of Carlos III of Madrid, 28911, Spain. · Technical University of Cartagena, 30202, Spain. · Institute of Artificial Intelligent at the University of Bremen, 28359, Germany.

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.

Figures & tables

Abbildung 1: Ontology model entity-relationship diagram

Abbildung 2: Extracting information from a relational data base.

Abbildung 3: Relational model representation of the SQL based system reasoner.

Abbildung 4: Relationships of the conceptual and physical hierarchy implemented in Protege.

Abbildung 5: Knowledge on which the comparison experiment between reasoners is carried out. The red lines correspond to data about the real world and the purple lines to conceptual relations.

		Output		Average execution time
Method	Input	OutputDB	OutputKR	Time using DB	Time using KR
Semantic labeling by object	Computer	Office	Office	4 ms	17 ms
Obtain Class Room from Instance Room	Room2	Kitchen	Kitchen	3 ms	20 ms
Obtain Class rooms containing an object	Chair	Living room	Living Room	2 ms	29 ms
		Office	Office
Obtain conceptually related objects	Soft drink	Refrigerator	Regrigerator	3 ms	50 ms

Tabelle I: Result of the comparison of the outputs between reasoners implemented with the two technologies.. (R): Refrigerator. (*) Complete list of conceptual objects, the order varies according to the reasoner. Each reasoner returns the 8 objects. (**) List of utilities, the order varies. Each reasoner returns 4 utilities.

Characteristic	Using KnowRob	Using database reasoner
Scalability	Good	Complicated
Execution Time	Higher	Lower
Application for navigation	Good	Very Good
Application for generic reasoning	Optimum	Possible

Tabelle II: Comparative of the implemented methods

Abbildung 7: Geometric map of environment.

Abbildung 8: Robot in a place for working.

Abbildung 9: Final position when the robot searches for a softdrink.

Robot Navigation

Robot navigation studies how robots perceive, localize, plan, and move safely toward goals in static or changing environments.

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From Language to Navigation Goals: A Vision-Language Approach for Semantic Navigation of Mobile Robots Using RGB-D Perception

Jose Martínez-Fajardo, Pablo Pueyo, Fernando Caballero, Luis Merino

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Robot Navigation Vision-Language Navigation Human-Robot Interaction Robotic Perception Object-Goal Navigation

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Natural language interaction provides an intuitive way for non-expert users to communicate with robotic platforms. However, transforming user requests into executable navigation actions remains a challenging task, requiring the integration of language understanding, environment perception, and autonomous navigation. This work presents a language-driven navigation framework that enables mobile robots to interpret user requests in natural language to move the robot to a destination and autonomously navigate towards it. The framework is composed of modular ROS 2 components that cooperate to transform natural language instructions into navigation actions. Given a natural language request referring to a target in the environment (e.g., "go to the mail box"), the system identifies the referenced object, estimates its position using RGB-D data, and generates a navigation goal, which is then executed through the ROS 2 Nav2 navigation stack. The ROS 2-based implementation facilitates portability across different robotic platforms, requiring only the configuration of the corresponding topics and services. The system is evaluated in both simulation and real-world scenarios using a TurtleBot3 Waffle and a Unitree Go2 robot with a RealSense camera. Experimental results show that the framework successfully interprets both direct commands and contextual requests, generates meaningful natural-language feedback, and navigates towards the desired target. These results demonstrate the feasibility of combining semantic perception and autonomous navigation to provide an intuitive human-robot interaction paradigm. Code will be released as open source upon acceptance.

Abstract

Robot Navigation

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