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.

翻译：三维实体对象的路径规划是一个具有挑战性的问题，需要在六维构型空间中进行搜索，但这在机器人分拣和装配等众多应用中至关重要。常用的基于采样的规划器，如快速探索随机树，在采样概率较低的狭窄通道中表现不佳，从而增加了找到解决方案所需的时间。在机器人分拣等场景中，各种物体必须通过相同的环境进行运输。然而，传统规划器每次都需要从头开始，丢失了规划过程中获得的宝贵信息。我们通过使用历史解决方案库来解决这一问题，使得即使在为新的、先前未见过的物体规划时，也能重用以往的经验。我们存储了一组物体的路径，当为新物体规划时，我们在库中找到最相似的物体，并将其路径作为近似解，同时针对可能的相互变换进行调整。随后，我们沿着近似路径在构型空间中进行采样。我们的方法在各种狭窄通道场景中进行了测试，并与OMPL库中的先进方法进行了比较。结果显示，我们的方法在速度上取得了显著提升（所需时间最多减少85%），并且经常在其他规划器失败的情况下找到解决方案。我们提出的方法的实现已作为开源软件包发布。