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.

翻译：经典路径规划器（如基于采样的路径规划器）存在对初始解敏感且收敛至最优解速度慢的局限性。然而，在电力/燃料受限的自动驾驶汽车等应用中，如何在短时间内获得近最优解仍面临挑战。为实现端到端的近最优路径规划，我们首先将路径规划问题分解为两个子问题：路径空间分割与给定路径空间中的航点生成。进一步地，我们提出一种名为路径规划网络（PPNet）的两级级联神经网络，通过求解上述子问题来解决路径规划问题。此外，我们提出一种名为EDaGe-PP的高效路径规划数据生成方法。实验结果表明：采用EDaGe-PP生成的数据集训练的PPNet，其总计算时间不足其他方法的1/33，且成功率约为其他方法的2倍。我们将PPNet与当前最先进的路径规划方法进行对比验证，结果显示PPNet能在15.3毫秒内找到近最优解，远优于现有路径规划器。