An Iterative Method Based on the Marginalized Particle Filter for Nonlinear B-Spline Data Approximation and Trajectory Optimization

Jauch, Jens; Bleimund, Felix; Frey, Michael; Gauterin, Frank

doi:10.3390/math7040355

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An Iterative Method Based on the Marginalized Particle Filter for Nonlinear B-Spline Data Approximation and Trajectory Optimization

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Institute of Vehicle System Technology, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany

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Mathematics 2019, 7(4), 355; https://doi.org/10.3390/math7040355

Received: 3 February 2019 / Revised: 1 April 2019 / Accepted: 10 April 2019 / Published: 16 April 2019

(This article belongs to the Special Issue Recent Trends in Multiobjective Optimization and Optimal Control)

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Abstract

The B-spline function representation is commonly used for data approximation and trajectory definition, but filter-based methods for nonlinear weighted least squares (NWLS) approximation are restricted to a bounded definition range. We present an algorithm termed nonlinear recursive B-spline approximation (NRBA) for an iterative NWLS approximation of an unbounded set of data points by a B-spline function. NRBA is based on a marginalized particle filter (MPF), in which a Kalman filter (KF) solves the linear subproblem optimally while a particle filter (PF) deals with nonlinear approximation goals. NRBA can adjust the bounded definition range of the approximating B-spline function during run-time such that, regardless of the initially chosen definition range, all data points can be processed. In numerical experiments, NRBA achieves approximation results close to those of the Levenberg–Marquardt algorithm. An NWLS approximation problem is a nonlinear optimization problem. The direct trajectory optimization approach also leads to a nonlinear problem. The computational effort of most solution methods grows exponentially with the trajectory length. We demonstrate how NRBA can be applied for a multiobjective trajectory optimization for a battery electric vehicle in order to determine an energy-efficient velocity trajectory. With NRBA, the effort increases only linearly with the processed data points and the trajectory length. View Full-Text

Keywords: nonlinear; recursive; iterative; B-spline; approximation; marginalized particle filter; Rao-Blackwellized particle filter; multiobjective; trajectory; optimization

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Jauch, J.; Bleimund, F.; Frey, M.; Gauterin, F. An Iterative Method Based on the Marginalized Particle Filter for Nonlinear B-Spline Data Approximation and Trajectory Optimization. Mathematics 2019, 7, 355. https://doi.org/10.3390/math7040355

AMA Style

Jauch J, Bleimund F, Frey M, Gauterin F. An Iterative Method Based on the Marginalized Particle Filter for Nonlinear B-Spline Data Approximation and Trajectory Optimization. Mathematics. 2019; 7(4):355. https://doi.org/10.3390/math7040355

Chicago/Turabian Style

Jauch, Jens, Felix Bleimund, Michael Frey, and Frank Gauterin. 2019. "An Iterative Method Based on the Marginalized Particle Filter for Nonlinear B-Spline Data Approximation and Trajectory Optimization" Mathematics 7, no. 4: 355. https://doi.org/10.3390/math7040355

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An Iterative Method Based on the Marginalized Particle Filter for Nonlinear B-Spline Data Approximation and Trajectory Optimization

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