LiU Electronic Press
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Author:
Wzorek, Mariusz (Linköping University, Department of Computer and Information Science, UASTECH - Autonomous Unmanned Aircraft Systems Technologies) (Linköping University, The Institute of Technology)
Kvarnström, Jonas (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab) (Linköping University, The Institute of Technology) (Automated Planning and Diagnosis Group)
Doherty, Patrick (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab) (Linköping University, Department of Computer and Information Science, UASTECH - Autonomous Unmanned Aircraft Systems Technologies) (Linköping University, The Institute of Technology)
Title:
Choosing Path Replanning Strategies for Unmanned Aircraft Systems
Department:
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab
Linköping University, The Institute of Technology
Linköping University, Department of Computer and Information Science, UASTECH - Autonomous Unmanned Aircraft Systems Technologies
Publication type:
Conference paper (Refereed)
Language:
English
In:
Proceedings of the Twentieth International Conference on Automated Planning and Scheduling (ICAPS)
Editor:
Ronen Brafman, Héctor Geffner, Jörg Hoffmann, Henry Kautz
Conference:
International Conference on Automated Planning and Scheduling (ICAPS)
Place of publ.: Toronto, Canada Publisher: AAAI Press
Pages:
193-200
Year of publ.:
2010
URI:
urn:nbn:se:liu:diva-59986
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-59986
ISBN:
978-1-57735-449-9
Subject category:
Computer Science
SVEP category:
Computer science
Keywords(en) :
artificial intelligence, path planning, motion planning, machine learning, autonomous unmanned vehicles, autonomous aircraft systems
Project:
Strategic Research Center MOVIII, Linnaeus Center CADICS, the Center for Industrial Information Technology CENIIT, the ELLIIT network for Information and Communication Technology, LinkLab, Swedish Research Council VR
Abstract(en) :

Unmanned aircraft systems use a variety of techniques to plan collision-free flight paths given a map of obstacles and no- fly zones. However, maps are not perfect and obstacles may change over time or be detected during flight, which may in- validate paths that the aircraft is already following. Thus, dynamic in-flight replanning is required.Numerous strategies can be used for replanning, where the time requirements and the plan quality associated with each strategy depend on the environment around the original flight path. In this paper, we investigate the use of machine learn- ing techniques, in particular support vector machines, to choose the best possible replanning strategy depending on the amount of time available. The system has been implemented, integrated and tested in hardware-in-the-loop simulation with a Yamaha RMAX helicopter platform.

Available from:
2010-10-01
Created:
2010-10-01
Last updated:
2013-08-29
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