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Robot Formations: Learning Minimum-Length Paths on Uneven Terrain

dc.contributor.authorHristu, Dimitriosen_US
dc.date.accessioned2007-05-23T10:10:27Z
dc.date.available2007-05-23T10:10:27Z
dc.date.issued2000en_US
dc.identifier.urihttp://hdl.handle.net/1903/6187
dc.description.abstractWe discuss a prototypeproblem involving terrain exploration and learning by formations ofautonomous vehicles. We investigate an algorithm forcoordinating multiple robots whose task is to find the shortest pathbetween a fixed pair of start and target locations, without access toa "global" map containing those locations.<p>Odometry information alone isnot sufficient for minimizing path length if the terrain is uneven orif it includes obstacles. We generalize existing results on a simplecontrol law, also known as "local pursuit," which is appropriate inthe context of formations and which requires limited interactionbetween vehicles. <p>Our algorithm is iterative and converges to alocally optimal path. We include simulations and experimentsillustrating the performance of the proposed strategy.<P><Center><I>The research and scientific content in this material has been published in the IEEE Mediterranean Conference on Control and Automation, July 2000.</I></Center>en_US
dc.format.extent491894 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 2000-25en_US
dc.relation.ispartofseriesCDCSS; TR 2000-7en_US
dc.subjectpath planningen_US
dc.subjectoptimizationen_US
dc.subjectroboticsen_US
dc.subjectformationsen_US
dc.subjectgeodesicsen_US
dc.subjectiterative learningen_US
dc.subjectautonomous vehiclesen_US
dc.subjectIntelligent Control Systemsen_US
dc.titleRobot Formations: Learning Minimum-Length Paths on Uneven Terrainen_US
dc.typeTechnical Reporten_US
dc.contributor.departmentISRen_US
dc.contributor.departmentCDCSSen_US


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