ITERATIVE METHODS FOR EFFICIENT SAMPLING-BASED OPTIMAL MOTION PLANNING OF NONLINEAR SYSTEMS

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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.
Publisher
UNIV ZIELONA GORA PRESS
Issue Date
2018-03
Language
English
Article Type
Article
Keywords

STOCHASTIC OPTIMAL-CONTROL; RRT-ASTERISK; CONSTRAINTS; ALGORITHMS

Citation

INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE, v.28, no.1, pp.155 - 168

ISSN
1641-876X
DOI
10.2478/amcs-2018-0012
URI
http://hdl.handle.net/10203/241442
Appears in Collection
AE-Journal Papers(저널논문)
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