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Issue No.05 - May (2011 vol.23)
pp: 759-773
Emilio Migueláñez , SeeByte, the Orchard Brae House, Edinburgh
Pedro Patrón , Heriot-Watt University, Edinburgh
Keith E. Brown , Heriot-Watt University, Edinburgh
Yvan R. Petillot , Heriot-Watt University, Edinburgh
David M. Lane , Heriot-Watt University, Edinburgh
This paper proposes a semantic world model framework for hierarchical distributed representation of knowledge in autonomous underwater systems. This framework aims to provide a more capable and holistic system, involving semantic interoperability among all involved information sources. This will enhance interoperability, independence of operation, and situation awareness of the embedded service-oriented agents for autonomous platforms. The results obtained specifically affect the mission flexibility, robustness, and autonomy. The presented framework makes use of the idea that heterogeneous real-world data of very different type must be processed by (and run through) several different layers, to be finally available in a suited format and at the right place to be accessible by high-level decision-making agents. In this sense, the presented approach shows how to abstract away from the raw real-world data step by step by means of semantic technologies. The paper concludes by demonstrating the benefits of the framework in a real scenario. A hardware fault is simulated in a REMUS 100 AUV while performing a mission. This triggers a knowledge exchange between the status monitoring agent and the adaptive mission planner embedded agent. By using the proposed framework, both services can interchange information while remaining domain independent during their interaction with the platform. The results of this paper are readily applicable to land and air robotics.
Autonomous vehicles, ontology design, model-based diagnostics, mission planning.
Emilio Migueláñez, Pedro Patrón, Keith E. Brown, Yvan R. Petillot, David M. Lane, "Semantic Knowledge-Based Framework to Improve the Situation Awareness of Autonomous Underwater Vehicles", IEEE Transactions on Knowledge & Data Engineering, vol.23, no. 5, pp. 759-773, May 2011, doi:10.1109/TKDE.2010.46
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