Please use this identifier to cite or link to this item:
Type: Artigo de periódico
Title: A penalized nonparametric method for nonlinear constrained optimization based on noisy data
Author: Dias, R
Garcia, NL
Zambom, AZ
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.
Subject: Autonomous vehicle
Consistent estimator
Confidence ellipses
Constrained optimization
Nonparametric method
Country: EUA
Editor: Springer
Rights: fechado
Identifier DOI: 10.1007/s10589-008-9185-6
Date Issue: 2010
Appears in Collections:Artigos e Materiais de Revistas Científicas - Unicamp

Files in This Item:
File Description SizeFormat 
WOS000275537800003.pdf3.56 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.