Motion planning is a hot topic in robotics, and the sampling-based algorithms have gained their popularities in research areas. However, these methods are still not suitable for real-world motion planning problems, because it is computationally expensive to completely explore the high-dimensional configuration space (C-space) of robots. Inspired by the related works on learning from demonstration, we propose a novel motion planning method named teaching roadmaps, which can take advantage of the optimal teaching data and quickly find a new path in the similar scenarios. The theoretical analysis and our experiments indicated that our approach is probabilistically complete, and it can find a feasible path faster than other sampling-based methods in similar environments.

中文翻译：

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具有教学路线图的半结构化环境中的运动计划

运动计划是机器人技术中的热门话题，基于采样的算法已在研究领域中广受欢迎。但是，这些方法仍然不适合现实世界中的运动计划问题，因为完全探索机器人的高维配置空间（C -space）在计算上是昂贵的。受到有关学习示范的相关工作的启发，我们提出了一种新颖的运动计划方法，即教学路线图，该方法可以利用最佳的教学数据并在类似的情况下快速找到新的路径。理论分析和我们的实验表明，我们的方法在概率上是完整的，并且在类似环境中比其他基于采样的方法可以更快地找到可行的路径。