Augmenting GRIPS with Heuristic Sampling for Planning Feasible Trajectories of a Car-Like Robot

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