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.
翻译:随着机器学习的最近进展,路径规划算法也在不断演变;然而,学习的路径规划算法往往难以与经典算法的成功率竞争。我们提出了路标规划算法(WPN),一种基于本地内核的LSTMs的混合算法(LSTMs),一种如A* 的经典算法,以及一种使用所学算法的全球内核。WPN产生了一种更高效和稳健的计算解决方案。我们比较了WPN与A* 的对比,以及相关工程,包括运动规划网络(MPNet)和价值迭代网络(VIN)。在本文中,为2D环境进行了设计和实验。实验结果概述了WPN在效率和一般化两方面的好处。显示WPN的搜索空间远远低于A*,同时能够产生接近最佳的结果。此外,WPN在部分地图上工作,而A* 则需要全部地图。代码可以在线查阅。