  Kinodynamic motion planning for non-holomonic mobile robots is a challenging
problem that is lacking a universal solution. One of the computationally
efficient ways to solve it is to build a geometric path first and then
transform this path into a kinematically feasible one. Gradient-informed Path
Smoothing (GRIPS) is a recently introduced method for such transformation.
GRIPS iteratively deforms the path and adds/deletes the waypoints while trying
to connect each consecutive pair of them via the provided steering function
that respects the kinematic constraints. The algorithm is relatively fast but,
unfortunately, does not provide any guarantees that it will succeed. In
practice, it often fails to produce feasible trajectories for car-like robots
with large turning radius. In this work, we introduce a range of modifications
that are aimed at increasing the success rate of GRIPS for car-like robots. The
main enhancement is adding the additional step that heuristically samples
waypoints along the bottleneck parts of the geometric paths (such as sharp
turns). The results of the experimental evaluation provide a clear evidence
that the success rate of the suggested algorithm is up to 40% higher compared
to the original GRIPS and hits the bar of 90%, while its runtime is lower.
