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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Monte Carlo algorithm for trajectory optimization based on Markovian readings

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Author(s):
Dias, Ronaldo [1] ; Garcia, Nancy L. [1] ; Zambom, Adriano Z. [1]
Total Authors: 3
Affiliation:
[1] IMECC UNICAMP, Dept Estat, BR-13081970 Campinas, SP - Brazil
Total Affiliations: 1
Document type: Journal article
Source: COMPUTATIONAL OPTIMIZATION AND APPLICATIONS; v. 51, n. 1, p. 305-321, JAN 2012.
Web of Science Citations: 2
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. (AU)