Sampling-based planners are usually thought of as offline
planners which try to find a trajectory through some hard narrow
passage. Typical problems include the need for possibly
unbounded amounts of memory and the generation of trajectories
that include a lot of redundant motions. Those problems are more
pronounced if a robot has realistic motion constraints (e.g.,
bounded acceleration) and physical effects such are gravity and
friction are modeled. This work describes a sampling-based
kinodynamic motion planner that solves hard problems using
replanning. The overall planning process is broken down into
replanning cycles. In each cycle, a sampling-based planner is
invoked for a small amount of time to produce a kinodynamic tree
of possible future motions. Then a navigation function is used
to evaluate these motions and select the one that brings the
robot closer to its goal. This approach has the advantage that
it uses only bounded memory. Moreover, it has been shown
experimentally that the produced paths ar