Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/76621
Type: Artigo
Title: A penalized nonparametric method for nonlinear constrained optimization based on noisy data
Author: Dias, Ronaldo
Garcia, Nancy L.
Zambom, Adriano Z.
Abstract: The objective of this study is to find a smooth function joining two points A and B with minimum length constrained to avoid fixed subsets. A penalized nonparametric method of finding the best path is proposed. The method is generalized to the situation where stochastic measurement errors are present. In this case, the proposed estimator is consistent, in the sense that as the number of observations increases the stochastic trajectory converges to the deterministic one. Two applications are immediate, searching the optimal path for an autonomous vehicle while avoiding all fixed obstacles between two points and flight planning to avoid threat or turbulence zones.
The objective of this study is to find a smooth function joining two points A and B with minimum length constrained to avoid fixed subsets. A penalized nonparametric method of finding the best path is proposed. The method is generalized to the situation w
Subject: Veículo autônomo
Spline, Teoria do
Ajuste de curva
Convergência (Matemática)
Otimização com restrições
Estatística não paramétrica
Country: Estados Unidos
Editor: Springer
Citation: Computational Optimization And Applications. Springer, v. 45, n. 3, n. 521, n. 541, 2010.
Rights: Aberto
Identifier DOI: 10.1007/s10589-008-9185-6
Address: https://link.springer.com/article/10.1007/s10589-008-9185-6
Date Issue: 2010
Appears in Collections:IMECC - Artigos e Outros Documentos

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