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Paper details
Number 1 - March 2018
Volume 28 - 2018
Iterative methods for efficient sampling-based optimal motion planning of nonlinear systems
Jung-Su Ha, Han-Lim Choi, Jeong Hwan Jeon
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.
Keywords
optimal motion planning, sampling-based algorithm, nonlinear dynamics