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.

翻译：经典的路径规划方法（如基于采样的路径规划器）能够提供概率完备性保证，即随着采样数量趋于无穷，规划器在存在可行解时未能返回解的概率趋近于零。然而，在自动驾驶等应用中，在给定时间内找到近优可行解仍然具有挑战性。为实现端到端的近优路径规划，我们首先将路径规划问题分解为两个子问题：路径空间分割和给定路径空间中的航路点生成。进一步，我们提出了一种名为路径规划网络（PPNet）的两阶段神经网络，每个阶段分别解决上述子问题。此外，我们提出了一种新颖高效的路径规划数据生成方法EDaGe-PP。EDaGe-PP能够生成具有连续曲率且满足避碰要求的解析表达式路径。结果表明，生成随机二维路径规划数据的总计算时间不足传统方法的1/33，且使用EDaGe-PP生成的数据集训练的PPNet成功率约为其他方法的两倍。我们与现有最先进的路径规划方法进行了对比验证。结果表明，PPNet能在15.3毫秒内找到近优解，这一时间远短于当前最先进的路径规划器。