  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 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 for computing 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.
