: A Knowledge Processing Middleware Framework
Heintz, Fredrik Rudol, Piotr Doherty, Patrick 2007 (English)In: Proceedings of the 30th Annual German Conference on Artificial Intelligence (KI) / [ed] Joachim Hertzberg, Michael Beetz and Roman Englert, Berlin, Heidelberg: Springer, 2007, 460-463Conference paper (Refereed)
To achieve complex missions an autonomous unmanned aerial vehicle (UAV) operating in dynamic environments must have and maintain situational awareness. This can be achieved by continually gathering information from many sources, selecting the relevant information for current tasks, and deriving models about the environment and the UAV itself. It is often the case models suitable for traditional control, are not sufficient for deliberation. The need for more abstract models creates a sense-reasoning gap. This paper presents DyKnow, a knowledge processing middleware framework, and shows how it supports bridging the gap in a concrete UAV traffic monitoring application. In the example, sequences of color and thermal images are used to construct and maintain qualitative object structures. They model the parts of the environment necessary to recognize traffic behavior of tracked vehicles in real-time. The system has been implemented and tested in simulation and on data collected during flight tests.
Lecture Notes in Computer Science, ISSN 0302-9743 ; 4667
knowledge representation, ai
National CategoryComputer Science
Identifiersurn:nbn:se:liu:diva-40832 (URN)10.1007/978-3-540-74565-5_40 (DOI)54282 (Local ID)978-3-540-74564-8 (ISBN)oai:DiVA.org:liu-40832 (OAI)diva2:261681 (DiVA)