PPNet: A Novel Neural Network Structure for End-to-End Near-Optimal Path
  Planning
Qinglong Meng Chongkun Xia Xueqian Wang Songping Mai Bin Liang
The classical path planners, such as sampling-based path planners, have the
limitations of sensitivity to the initial solution and slow convergence to the
optimal solution. However, finding a near-optimal solution in a short period is
challenging in many applications such as the autonomous vehicle with limited
power/fuel. To achieve an end-to-end near-optimal path planner, we first divide
the path planning problem into two subproblems, which are path's space
segmentation and waypoints generation in the given path's space. We further
propose a two-level cascade neural network named Path Planning Network (PPNet)
to solve the path planning problem by solving the abovementioned subproblems.
Moreover, we propose a novel efficient data generation method for path planning
named EDaGe-PP. The results show the total computation time 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 PPNet can find
a near-optimal solution in 15.3ms, which is much shorter than the
state-of-the-art path planners.