With the recent advances in machine learning, path planning algorithms are also evolving; however, the learned path planning algorithms often have difficulty competing with success rates of classic algorithms. We propose waypoint planning networks (WPN), a hybrid algorithm based on LSTMs with a local kernel - a classic algorithm such as A*, and a global kernel using a learned algorithm. WPN produces a more computationally efficient and robust solution. We compare WPN against A*, as well as related works including motion planning networks (MPNet) and value iteration networks (VIN). In this paper, the design and experiments have been conducted for 2D environments. Experimental results outline the benefits of WPN, both in efficiency and generalization. It is shown that WPN's search space is considerably less than A*, while being able to generate near optimal results. Additionally, WPN works on partial maps, unlike A* which needs the full map in advance. The code is available online.

中文翻译：

---

航点规划网络

随着机器学习的最新发展，路径规划算法也在不断发展。然而，学习的路径规划算法通常难以与经典算法的成功率竞争。我们提出了航路点规划网络（WPN），基于LSTM和本地内核的混合算法-一种经典算法（例如A *）和使用学习算法的全局内核。WPN产生了一个计算效率更高且更健壮的解决方案。我们将WPN与A *以及包括运动计划网络（MPNet）和价值迭代网络（VIN）在内的相关作品进行了比较。在本文中，已经针对2D环境进行了设计和实验。实验结果概述了WPN在效率和泛化方面的优势。结果表明，WPN的搜索空间大大小于A *，同时能够产生接近最佳的结果。此外，WPN可以在部分地图上工作，与A *不同，后者需要事先完整的地图。该代码可在线获得。