Error Detection and Recovery for Robot Motion Planning with Uncertainty

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6851

Full metadata record

DC Field	Value	Language
dc.creator	Donald, Bruce Randall	-
dc.date	2004-10-20T20:02:29Z	-
dc.date	2004-10-20T20:02:29Z	-
dc.date	1987-07-01	-
dc.date.accessioned	2013-10-09T02:47:13Z	-
dc.date.available	2013-10-09T02:47:13Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AITR-982	-
dc.identifier	http://hdl.handle.net/1721.1/6851	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	Robots must plan and execute tasks in the presence of uncertainty. Uncertainty arises from sensing errors, control errors, and uncertainty in the geometry of the environment. The last, which is called model error, has received little previous attention. We present a framework for computing motion strategies that are guaranteed to succeed in the presence of all three kinds of uncertainty. The motion strategies comprise sensor-based gross motions, compliant motions, and simple pushing motions.	-
dc.format	310 p.	-
dc.format	44428054 bytes	-
dc.format	35921531 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AITR-982	-
dc.subject	robotics	-
dc.subject	motion planning	-
dc.subject	uncertainty	-
dc.subject	error detection andsrecovery	-
dc.subject	computational geometry	-
dc.subject	geometric reasoning	-
dc.subject	planning withsuncertainty	-
dc.subject	model error	-
dc.subject	EDR	-
dc.subject	failure mode analysis	-
dc.title	Error Detection and Recovery for Robot Motion Planning with Uncertainty	-
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