LiU Electronic Press
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Author:
Lazarovski, Daniel (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab) (Linköping University, The Institute of Technology)
Title:
Extending the Stream Reasoning in DyKnow with Spatial Reasoning in RCC-8
Department:
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab
Linköping University, The Institute of Technology
Publication type:
Student thesis
Language:
English
Level:
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
Undergraduate subject:
Master's programme in Computer Science
Uppsok:
Technology
Pages:
74
Year of publ.:
2012
URI:
urn:nbn:se:liu:diva-75885
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-75885
ISRN:
LIU-IDA/LITH-EX-A--12/008--SE
Subject category:
Computer Science
Keywords(en) :
Qualitative Spatial Reasoning, Qualitative Spatio-Temporal Reasoning, RCC-8, DyKnow, Stream Reasoning, ROS, Knowledge Representation
Project:
Collaborative Unmanned Aircraft Systems (CUAS)
Abstract(en) :

Autonomous systems require a lot of information about the environment in which they operate in order to perform different high-level tasks. The information is made available through various sources, such as remote and on-board sensors, databases, GIS, the Internet, etc. The sensory input especially is incrementally available to the systems and can be represented as streams. High-level tasks often require some sort of reasoning over the input data, however raw streaming input is often not suitable for the higher level representations needed for reasoning. DyKnow is a stream processing framework that provides functionalities to represent knowledge needed for reasoning from streaming inputs. DyKnow has been used within a platform for task planning and execution monitoring for UAVs. The execution monitoring is performed using formula progression with monitor rules specified as temporal logic formulas. In this thesis we present an analysis for providing spatio-temporal functionalities to the formula progressor and we extend the formula progression with spatial reasoning in RCC-8. The result implementation is capable of evaluating spatio-temporal logic formulas using progression over streaming data. In addition, a ROS implementation of the formula progressor is presented as a part of a spatio-temporal stream reasoning architecture in ROS.

Presentation:
2012-02-06, Kurt Gödel, Linköping University, Linköping, 15:15 (English)
Supervisor:
Heintz, Fredrik, PhD (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab) (Linköping University, The Institute of Technology)
Examiner:
Heintz, Fredrik, PhD
Available from:
2012-03-28
Created:
2012-03-14
Last updated:
2014-03-27
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