用历史路径库加速机器人3D路径规划，大幅缩短搜索时间。
Enhancing Sampling-based Planning with a Library of Paths
- 构建路径库复用过往规划经验，新物体直接参考相似路径。
- 在狭窄通道场景中提速最高达85%，且成功率显著提升。
- 适合重复性任务如工业抓取，开源实现便于落地应用。
针对三维实体的路径规划问题，传统采样方法（如RRT）在狭窄通道中因采样概率低而效率低下。在类似机器人抓取的任务中，多个物体需通过相同环境，但现有方法每次均从零开始规划，无法复用历史信息。本文提出使用路径库存储已规划路径，当为新物体规划时，检索库中最相似的路径作为近似解，并基于可能的变换进行调整，再沿该近似路径进行采样。在多种狭窄通道场景下测试，与OMPL库中的先进方法对比，本方法最大可减少85%的规划时间，且在部分其他方法失败的情况下仍能成功找到路径。代码已开源。
原文摘要 · Abstract (English)
Path planning for 3D solid objects is a challenging problem, requiring a search in a six-dimensional configuration space, which is, nevertheless, essential in many robotic applications such as bin-picking and assembly. The commonly used sampling-based planners, such as Rapidly-exploring Random Trees, struggle with narrow passages where the sampling probability is low, increasing the time needed to find a solution. In scenarios like robotic bin-picking, various objects must be transported through the same environment. However, traditional planners start from scratch each time, losing valuable information gained during the planning process. We address this by using a library of past solutions, allowing the reuse of previous experiences even when planning for a new, previously unseen object. Paths for a set of objects are stored, and when planning for a new object, we find the most similar one in the library and use its paths as approximate solutions, adjusting for possible mutual transformations. The configuration space is then sampled along the approximate paths. Our method is tested in various narrow passage scenarios and compared with state-of-the-art methods from the OMPL library. Results show significant speed improvements (up to 85% decrease in the required time) of our method, often finding a solution in cases where the other planners fail. Our implementation of the proposed method is released as an open-source package.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。