期刊名称：International Journal of Applied Mathematics and Computer Science
电子版ISSN：2083-8492
出版年度：2018
卷号：28
期号：1
页码：1-14
DOI：10.2478/amcs-2018-0012
出版社：De Gruyter Open
摘要：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.
