Predicting Partial Paths from Planning Problem Parameters
Predicting Partial Paths from Planning Problem Parameters
复制标题
根据规划问题参数预测部分路径
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
Tomas Lozano
中科院分区：
文献类型：
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作者：
S. Finney;L. Kaelbling;Tomas Lozano
许多机器人运动规划问题可描述为在构形空间相对稀疏区域的运动以及通过狭窄通道的运动的组合。基于采样的规划器在除狭窄通道外的各处都非常有效。在本文中，我们提供了一种参数化描述对规划器而言困难的工作空间布局的方法，然后学习一个函数，该函数根据参数提出穿过它们的部分路径。然后，这些建议的部分路径被用于显著加快对新问题的规划。
Many robot motion planning problems can be described as a combination of motion through relatively sparsely filled regions of configuration space and motion through tighter passages. Sample-based planners perform very effectively everywhere but in the tight passages. In this paper, we provide a method for parametrically describing workspace arrangements that are difficult for planners, and then learning a function that proposes partial paths through them as a function of the parameters. These suggested partial paths are then used to significantly speed up planning for new problems.