An ontology-based approach to improve the accessibility of ROS-based robotic systems

Ilaria Tiddi*, Emanuele Bastianelli, Gianluca Bardaro, Mathieu D'Aquin, Enrico Motta

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

The focus of this work is to exploit ontologies to make robotic systems more accessible to non-expert users, therefore supporting the deployment of robot-integrated applications. Due to the increasing number of robotic platforms available for commercial use, robotic systems are nowadays being approached by users with different backgrounds, who are often more interested in the robots' high-level capabilities than their technical architecture. Without the right expertise however, using robots is restricted to the capabilities exposed by the platform provider, i.e. they can only be used as end products rather than as development platforms. Our hypothesis is that an ontological representation of the capabilities of robots could make these capabilities more accessible, reducing the complexity of robot programming and enabling non-experts to exploit these systems to a much larger extent. To demonstrate this, an ontology abstracting the capabilities exposed by the most common robotic middleware (ROS) is integrated in a system to allow non-experts to program robots of different types and capabilities without previous knowledge either of the specific robotic platform being considered, or of the intricate systems used in its implementation. Our experiments, in which non-experts users had to configure the system in order to make robots achieve different tasks, show how the efforts required for realizing basic tasks using available robotic platforms can be sensibly reduced through our approach.

Original languageEnglish
Title of host publicationProceedings of the Knowledge Capture Conference, K-CAP 2017
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450355537
DOIs
Publication statusPublished - 4 Dec 2017
Externally publishedYes
Event9th International Conference on Knowledge Capture, K-CAP 2017 - Austin, United States
Duration: 4 Dec 20176 Dec 2017

Publication series

NameProceedings of the Knowledge Capture Conference, K-CAP 2017

Conference

Conference9th International Conference on Knowledge Capture, K-CAP 2017
CountryUnited States
CityAustin
Period4/12/176/12/17

Keywords

  • Artificial Intelligence
  • Knowledge Representation
  • Robotics

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