  The classical path planners, such as sampling-based path planners, can
provide probabilistic completeness guarantees in the sense that the probability
that the planner fails to return a solution if one exists, decays to zero as
the number of samples approaches infinity. However, finding a near-optimal
feasible solution in a given period is challenging in many applications such as
the autonomous vehicle. To achieve an end-to-end near-optimal path planner, we
first divide the path planning problem into two subproblems, which are path
space segmentation and waypoints generation in the given path's space. We
further propose a two-stage neural network named Path Planning Network (PPNet)
each stage solves one of the subproblems abovementioned. Moreover, we propose a
novel efficient data generation method for path planning named EDaGe-PP.
EDaGe-PP can generate data with continuous-curvature paths with analytical
expression while satisfying the clearance requirement. The results show the
total computation time of generating random 2D path planning data is less than
1/33 and the success rate of PPNet trained by the dataset that is generated by
EDaGe-PP is about 2 times compared to other methods. We validate PPNet against
state-of-the-art path planning methods. The results show that PPNet can find a
near-optimal solution in 15.3ms, which is much shorter than the
state-of-the-art path planners.
