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Error Detection and Recovery for Robot Motion Planning with Uncertainty

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.

Identiferoai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/6851
Date01 July 1987
CreatorsDonald, Bruce Randall
Source SetsM.I.T. Theses and Dissertation
Languageen_US
Detected LanguageEnglish
Format310 p., 44428054 bytes, 35921531 bytes, application/postscript, application/pdf
RelationAITR-982

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