  Sampling-based motion planning algorithms such as RRT* are well-known for
their ability to quickly find an initial solution and then converge to the
optimal solution asymptotically. However, the convergence rate can be slow for
highdimensional planning problems, particularly for dynamical systems where the
sampling space is not just the configuration space but the full state space. In
this paper, we introduce the idea of using a partial-final-state-free (PFF)
optimal controller in kinodynamic RRT* [1] to reduce the dimensionality of the
sampling space. Instead of sampling the full state space, the proposed
accelerated kinodynamic RRT*, called Kino-RRT*, only samples part of the state
space, while the rest of the states are selected by the PFF optimal controller.
We also propose a delayed and intermittent update of the optimal arrival time
of all the edges in the RRT* tree to decrease the computation complexity of the
algorithm. We tested the proposed algorithm using 4-D and 10-D state-space
linear systems and showed that Kino-RRT* converges much faster than the
kinodynamic RRT* algorithm.
