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

Tiddi, Ilaria; Bastianelli, Emanuele; Bardaro, Gianluca; d'Aquin, Mathieu and Motta, Enrico (2017). An ontology-based approach to improve the accessibility of ROS-based robotic systems. In: Proceedings of the Knowledge Capture Conference on - K-CAP 2017, pp. 1–8.

DOI (Digital Object Identifier) Link: https://doi.org/10.1145/3148011.3148014
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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.

Item Type: Conference or Workshop Item
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 52931
Depositing User: Kay Dave
Date Deposited: 19 Jan 2018 15:07
Last Modified: 28 Mar 2018 09:59
URI: http://oro.open.ac.uk/id/eprint/52931
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