<abstract xmlns="http://www.w3.org/1999/xhtml"><p> This paper extends the RRT* algorithm, a recently developed but widely used sampling based optimal motion planner, in order to effectively handle nonlinear kinodynamic constraints. Nonlinearity in kinodynamic differential constraints often leads to difficulties in choosing an appropriate distance metric and in computing optimized trajectory segments in tree construction. To tackle these two difficulties, this work adopts the affine quadratic regulator-based pseudo-metric as the distance measure and utilizes iterative two-point boundary value problem solvers to compute the optimized segments. The proposed extension then preserves the inherent asymptotic optimality of the RRT* framework, while efficiently handling a variety of kinodynamic constraints. Three numerical case studies validate the applicability of the proposed method.</p></abstract>

This paper extends the RRT* algorithm, a recently developed but widely used sampling based optimal motion planner, in order to effectively handle nonlinear kinodynamic constraints. Nonlinearity in kinodynamic differential constraints often leads to difficulties in choosing an appropriate distance metric and in computing optimized trajectory segments in tree construction. To tackle these two difficulties, this work adopts the affine quadratic regulator-based pseudo-metric as the distance measure and utilizes iterative two-point boundary value problem solvers to compute the optimized segments. The proposed extension then preserves the inherent asymptotic optimality of the RRT* framework, while efficiently handling a variety of kinodynamic constraints. Three numerical case studies validate the applicability of the proposed method.

Iterative methods for efficient sampling-based optimal motion planning of nonlinear systems

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

This paper extends the RRT* algorithm, a recently developed but widely used sampling based optimal motion planner, in order to effectively handle nonlinear...

Iterative methods for efficient sampling-based optimal motion planning of nonlinear systems

Online veröffentlicht: 31. März 2018

Seitenbereich: 155 - 168

Eingereicht: 09. Jan. 2017

Akzeptiert: 01. Sept. 2017

DOI: https://doi.org/10.2478/amcs-2018-0012

Schlüsselwörter
optimal motion planning, sampling-based algorithm, nonlinear dynamics

© by Han-Lim Choi

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Iterative methods for efficient sampling-based optimal motion planning of nonlinear systems

Online veröffentlicht: 31. März 2018

Seitenbereich: 155 - 168

Eingereicht: 09. Jan. 2017

Akzeptiert: 01. Sept. 2017

DOI: https://doi.org/10.2478/amcs-2018-0012

Schlüsselwörteroptimal motion planning, sampling-based algorithm, nonlinear dynamics

© by Han-Lim Choi

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
optimal motion planning, sampling-based algorithm, nonlinear dynamics