  This paper aims to improve the path quality and computational efficiency of
kinodynamic planners used for vehicular systems. It proposes a learning
framework for identifying promising controls during the expansion process of
sampling-based motion planners for systems with dynamics. Offline, the learning
process is trained to return the highest-quality control that reaches a local
goal state (i.e., a waypoint) in the absence of obstacles from an input
difference vector between its current state and a local goal state. The data
generation scheme provides bounds on the target dispersion and uses state space
pruning to ensure high-quality controls. By focusing on the system's dynamics,
this process is data efficient and takes place once for a dynamical system, so
that it can be used for different environments with modular expansion
functions. This work integrates the proposed learning process with a) an
exploratory expansion function that generates waypoints with biased coverage
over the reachable space, and b) proposes an exploitative expansion function
for mobile robots, which generates waypoints using medial axis information.
This paper evaluates the learning process and the corresponding planners for a
first and second-order differential drive systems. The results show that the
proposed integration of learning and planning can produce better quality paths
than kinodynamic planning with random controls in fewer iterations and
computation time.
