Sensory Uncertainty Field model for unknown and non-stationary mobile robot environments

Vlassis, NA; Tsanakas, P

dc.contributor.author	Vlassis, NA	en
dc.contributor.author	Tsanakas, P	en
dc.date.accessioned	2014-03-01T02:41:31Z
dc.date.available	2014-03-01T02:41:31Z
dc.date.issued	1998	en
dc.identifier.issn	10504729	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30506
dc.subject	Mobile Robot	en
dc.subject	Path Planning	en
dc.subject.other	Algorithms	en
dc.subject.other	Approximation theory	en
dc.subject.other	Computational geometry	en
dc.subject.other	Kalman filtering	en
dc.subject.other	Motion planning	en
dc.subject.other	Neural networks	en
dc.subject.other	Delaunay triangulation	en
dc.subject.other	Robot localization	en
dc.subject.other	Sensory uncertainty field (SUF)	en
dc.subject.other	Mobile robots	en
dc.title	Sensory Uncertainty Field model for unknown and non-stationary mobile robot environments	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ROBOT.1998.676428	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ROBOT.1998.676428	en
heal.publicationDate	1998	en
heal.abstract	A Sensory Uncertainty Field (SUF) is a model of the localization uncertainty of a mobile robot. The value of the SUF at a specific robot configuration q expresses the expected uncertainty of the robot at q, as this would be measured by some localization procedure. Path planning over the SUF provides a way for better localization, and thus fewer failures, during navigation. In this paper we extend the original notion of a SUF to unknown and non-stationary environments. We propose a self-organizing neural network model that is capable of building and maintaining an estimation of the SUF while the robot moves around its free space, based on some dynamic localization information, e.g., Kalman filtering. The attractive feature of our algorithm is its capability of handling both unknown and dynamic, i.e., non-stationary, environments. We present a method for polygonal approximation of the resulting SUF by using the Delaunay triangulation.	en
heal.publisher	IEEE, Piscataway, NJ, United States	en
heal.journalName	Proceedings - IEEE International Conference on Robotics and Automation	en
dc.identifier.doi	10.1109/ROBOT.1998.676428	en
dc.identifier.volume	1	en
dc.identifier.spage	363	en
dc.identifier.epage	368	en