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Type: Artigo de periódico
Title: Monte Carlo Algorithm For Trajectory Optimization Based On Markovian Readings
Author: Dias R.
Garcia N.L.
Zambom A.Z.
Abstract: This paper describes an efficient algorithm to find a smooth trajectory joining two points A and B with minimum length constrained to avoid fixed subsets. The basic assumption is that the locations of the obstacles are measured several times through a mechanism that corrects the sensors at each reading using the previous observation. The proposed algorithm is based on the penalized nonparametric method previously introduced that uses confidence ellipses as a fattening of the avoidance set. In this paper we obtain consistent estimates of the best trajectory using Monte Carlo construction of the confidence ellipse. © Springer Science+Business Media, LLC 2010.
Rights: fechado
Identifier DOI: 10.1007/s10589-010-9337-3
Date Issue: 2012
Appears in Collections:Unicamp - Artigos e Outros Documentos

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