  Anytime sampling-based methods are an attractive technique for solving
kino-dynamic motion planning problems. These algorithms scale well to higher
dimensions and can efficiently handle state and control constraints. However,
an intelligent exploration strategy is required to accelerate their convergence
and avoid redundant computations. Using ideas from reachability analysis, this
work defines a "Time-Informed Set", that focuses the search for time-optimal
kino-dynamic planning after an initial solution is found. Such a Time-Informed
Set (TIS) includes all trajectories that can potentially improve the current
best solution and hence exploration outside this set is redundant. Benchmarking
experiments show that an exploration strategy based on the TIS can accelerate
the convergence of sampling-based kino-dynamic motion planners.
