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Monte Carlo algorithm for trajectory optimization based on Markovian readings

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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.

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Authors and Affiliations

  1. Departamento de Estatística, IMECC/UNICAMP, Caixa Postal 6065, 13081-970, Campinas, SP, Brazil

    Ronaldo Dias, Nancy L. Garcia & Adriano Z. Zambom

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  1. Ronaldo Dias
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  2. Nancy L. Garcia
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  3. Adriano Z. Zambom
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Correspondence to Ronaldo Dias.

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Dias, R., Garcia, N.L. & Zambom, A.Z. Monte Carlo algorithm for trajectory optimization based on Markovian readings. Comput Optim Appl 51, 305–321 (2012). https://doi.org/10.1007/s10589-010-9337-3

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  • DOI: https://doi.org/10.1007/s10589-010-9337-3

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