  Sampling-based motion planners are an effective means for generating
collision-free motion paths. However, the quality of these motion paths, with
respect to different quality measures such as path length, clearance,
smoothness or energy, is often notoriously low. This problem is accentuated in
the case of non-holonomic sampling-based motion planning, in which the space of
feasible motion trajectories is restricted. In this study, we combine the C-PRM
algorithm by Song and Amato with our recently introduced path-hybridization
approach, for creating high quality non-holonomic motion paths, with
combinations of several different quality measures such as path length,
smoothness or clearance, as well as the number of reverse car motions. Our
implementation includes a variety of code optimizations that result in nearly
real-time performance, and which we believe can be extended with further
optimizations to a real-time tool for the planning of high-quality car-like
motion.
