A penalized nonparametric method for nonlinear constrained optimization based on noisy data

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.

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Correspondence to Ronaldo Dias.

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Dias, R., Garcia, N.L. & Zambom, A.Z. A penalized nonparametric method for nonlinear constrained optimization based on noisy data. Comput Optim Appl 45, 521–541 (2010). https://doi.org/10.1007/s10589-008-9185-6

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Keywords

  • Autonomous vehicle
  • B-splines
  • Consistent estimator
  • Confidence ellipses
  • Constrained optimization
  • Nonparametric method