  This paper aims to improve the computational efficiency of motion planning
for mobile robots with non-trivial dynamics through the use of learned
controllers. It adopts a decoupled strategy, where a system-specific controller
is first trained offline in an empty environment to deal with the robot's
dynamics. For a target environment, the proposed approach constructs offline a
data structure, a "Roadmap with Gaps," to approximately learn how to solve
planning queries in this environment using the learned controller. The nodes of
the roadmap correspond to local regions. Edges correspond to applications of
the learned control policy that approximately connect these regions. Gaps arise
because the controller does not perfectly connect pairs of individual states
along edges. Online, given a query, a tree sampling-based motion planner uses
the roadmap so that the tree's expansion is informed towards the goal region.
The tree expansion selects local subgoals given a wavefront on the roadmap that
guides towards the goal. When the controller cannot reach a subgoal region, the
planner resorts to random exploration to maintain probabilistic completeness
and asymptotic optimality. The accompanying experimental evaluation shows that
the approach significantly improves the computational efficiency of motion
planning on various benchmarks, including physics-based vehicular models on
uneven and varying friction terrains as well as a quadrotor under air pressure
effects.
