  This paper presents an equivalence between feasible kinodynamic planning and
optimal kinodynamic planning, in that any optimal planning problem can be
transformed into a series of feasible planning problems in a state-cost space
whose solutions approach the optimum. This transformation gives rise to a
meta-algorithm that produces an asymptotically optimal planner, given any
feasible kinodynamic planner as a subroutine. The meta-algorithm is proven to
be asymptotically optimal, and a formula is derived relating expected running
time and solution suboptimality. It is directly applicable to a wide range of
optimal planning problems because it does not resort to the use of steering
functions or numerical boundary-value problem solvers. On a set of benchmark
problems, it is demonstrated to perform, using the EST and RRT algorithms as
subroutines, at a superior or comparable level to related planners.
