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|Type:||Artigo de periódico|
|Title:||Monte Carlo Algorithm For Trajectory Optimization Based On Markovian Readings|
|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.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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