[en] Software development for robots is a knowledge intensive exercise. To capture this knowledge explicitly and formally in the form of various domain models, roboticists have recently employed model-driven engineering (MDE) approaches. However, these models are merely seen as a way to support humans during the robot's software design process. We argue that the robots themselves should be first-class consumers of this knowledge to autonomously adapt their software to the various and changing run-time requirements induced, for instance, by the robot's tasks or environment. Motivated by knowledge-enabled approaches, we address this problem by employing a graph-based knowledge representation that allows us not only to persistently store domain models, but also to formulate powerful queries for the sake of run time adaptation. We have evaluated our approach in an integrated, real-world system using the neo4j graph database and we report some lessons learned. Further, we show that the graph database imposes only little overhead on the system's overall performance.
Disciplines :
Computer science
Author, co-author :
Hochgeschwender, Nico ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Schneider, Sven
Voos, Holger ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Bruyninckx, Herman
Kraetzschmar, Gerhard
External co-authors :
yes
Language :
English
Title :
Graph-based Software Knowledge: Storage and Semantic Querying of Domain Models for Run-Time Adaptation
Publication date :
December 2016
Event name :
IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR)
Event organizer :
IEEE
Event place :
San Francisco, United States
Event date :
Dec 13-16, 2016
Audience :
International
Main work title :
IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots SIMPAR, San Francisco, Dec 2016
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