Global path planning for autonomous qualitative navigation

Vlassis, NA; Sgouros, NM; Efthivoulidis, G; Papakonstantinou, G; Tsanakas, P

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