Global path planning for autonomous qualitative navigation

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dc.contributor.author Vlassis, NA en
dc.contributor.author Sgouros, NM en
dc.contributor.author Efthivoulidis, G en
dc.contributor.author Papakonstantinou, G en
dc.contributor.author Tsanakas, P en
dc.date.accessioned 2014-03-01T02:41:13Z
dc.date.available 2014-03-01T02:41:13Z
dc.date.issued 1996 en
dc.identifier.issn 10636730 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/30425
dc.subject Indoor Environment en
dc.subject Path Planning en
dc.subject.other Algorithms en
dc.subject.other Artificial intelligence en
dc.subject.other Computer simulation en
dc.subject.other Motion control en
dc.subject.other Motion planning en
dc.subject.other Navigation en
dc.subject.other Proximity sensors en
dc.subject.other Real time systems en
dc.subject.other Wheelchairs en
dc.subject.other Global path planning en
dc.subject.other Robotic wheelchairs en
dc.subject.other Shortest path algorithms en
dc.subject.other Mobile robots en
dc.title Global path planning for autonomous qualitative navigation en
heal.type conferenceItem en
heal.identifier.primary 10.1109/TAI.1996.560476 en
heal.identifier.secondary http://dx.doi.org/10.1109/TAI.1996.560476 en
heal.publicationDate 1996 en
heal.abstract We describe a novel global path planning method for autonomous qualitative navigation in indoor environments. Global path planning operates on top of a qualitative map of the environment that describes variations in sensor behavior between adjacent regions in space. The method takes into consideration the global topology of the environment and applies a set of criteria that can minimize the errors in the navigational accuracy of a robotic wheelchair. Our approach uses a modified version of the Dijkstra's shortest path algorithm that takes into consideration the curvature of the trajectory and the off-wall distance of the map points. The algorithm computes in real-time a set of optimal paths for reaching the destination. We have tested our global path planning method in simulation in representative indoor environments with above average complexity. Based on these experiments we have determined empirically a set of values for the parameters of the algorithm that almost always lead to the selection of optimal paths in these environments. en
heal.publisher IEEE, Piscataway, NJ, United States en
heal.journalName Proceedings of the International Conference on Tools with Artificial Intelligence en
dc.identifier.doi 10.1109/TAI.1996.560476 en
dc.identifier.spage 354 en
dc.identifier.epage 359 en

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