Wzorek, Mariusz Kvarnström, Jonas Doherty, Patrick 2010 (English)In: Proceedings of the Twentieth International Conference on Automated Planning and Scheduling (ICAPS) / [ed] Ronen Brafman, Héctor Geffner, Jörg Hoffmann, Henry Kautz, Toronto, Canada: AAAI Press, 2010, 193-200Konferensbidrag (Refereed)
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.
artificial intelligence, path planning, motion planning, machine learning, autonomous unmanned vehicles, autonomous aircraft systems
National CategoryComputer Science
Identifiersurn:nbn:se:liu:diva-59986 (URN)978-1-57735-449-9 (ISBN)oai:DiVA.org:liu-59986 (OAI)
International Conference on Automated Planning and Scheduling (ICAPS)
ProjectsStrategic Research Center MOVIIILinnaeus Center CADICSthe Center for Industrial Information Technology CENIITthe ELLIIT network for Information and Communication TechnologyLinkLabSwedish Research Council VR