HEAL DSpace

Localized qualitative navigation for indoor environments

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dc.contributor.author Sgouros, NM en
dc.contributor.author Papakonstantinou, G en
dc.contributor.author Tsanakas, P en
dc.date.accessioned 2014-03-01T02:41:14Z
dc.date.available 2014-03-01T02:41:14Z
dc.date.issued 1996 en
dc.identifier.issn 10504729 en
dc.identifier.uri http://hdl.handle.net/123456789/30429
dc.subject Indoor Environment en
dc.subject Linear Time en
dc.subject Numerical Simulation en
dc.subject Obstacle Detection en
dc.subject Path Planning en
dc.subject Position Estimation en
dc.subject Short Term Memory en
dc.subject.other Adaptive control systems en
dc.subject.other Collision avoidance en
dc.subject.other Computer simulation en
dc.subject.other Motion planning en
dc.subject.other Navigation en
dc.subject.other Online systems en
dc.subject.other Parameter estimation en
dc.subject.other Position control en
dc.subject.other Sensors en
dc.subject.other Topology en
dc.subject.other Indoor navigation en
dc.subject.other Localization en
dc.subject.other Obstacle detection en
dc.subject.other Obstacle orientation en
dc.subject.other Offline planner en
dc.subject.other Online controller en
dc.subject.other Position estimation en
dc.subject.other Mobile robots en
dc.title Localized qualitative navigation for indoor environments en
heal.type conferenceItem en
heal.identifier.primary 10.1109/ROBOT.1996.503890 en
heal.identifier.secondary http://dx.doi.org/10.1109/ROBOT.1996.503890 en
heal.publicationDate 1996 en
heal.abstract We describe a novel architecture for indoor navigation, based on qualitative representations of the variations in the interactions between the robot and its environment. We use these representations to localize and guide planning and reaction. Off-line, the system accepts as input a topological diagram of the environment. It then uses numerical simulation to generate a map, describing qualitative variations in sensor behavior between adjacent regions in space. An off-line planner stores localized navigation information at each point in the map. During execution, an adaptive controller uses a short-term memory to improve its operation. The qualitative nature of our method, along with the localization performed by the topological planner, result in a compact map representation and in linear-time performances for position estimation and path planning during execution. We have tested this architecture in simulation. Our results show that the proposed navigation method is tolerant of sensor inaccuracies, both in obstacle detection and orientation. 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.1996.503890 en
dc.identifier.volume 1 en
dc.identifier.spage 921 en
dc.identifier.epage 926 en


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