Hierarchical Motion Planning in Topological Representations
Hierarchical Motion Planning in Topological Representations
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拓扑表示中的分层运动规划
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
S. Vijayakumar
中科院分区：
文献类型：
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作者：
D. Zarubin;V. Ivan;Marc Toussaint;T. Komura;S. Vijayakumar
运动可以用不同的表示来描述，包括关节构型或末端执行器空间，也可以用更复杂的拓扑表示来描述，这些表示暗示了运动空间的Voronoi偏差、度量或拓扑的变化。某些类型的机器人交互问题，例如缠绕对象，可以通过所谓的扭曲和交互网格表示来适当地描述。然而，仅在拓扑空间中考虑运动综合是不够的，因为它不能考虑其他表示中的附加任务和约束。在本文中，我们提出了结合和利用不同表示法来进行运动合成的方法，特别强调将运动推广到新的情况。我们的方法是在最优控制的框架下作为一个近似推理问题来表示的，它允许图形模型的直接扩展以包含多种表示。运动泛化类似地通过将运动从拓扑结构空间投影到关节构形空间来执行。我们展示了我们的方法的好处，在关节配置空间中直接寻找路径是非常困难的，而利用不同拓扑表示的局部最优控制可以有效地找到最优轨迹。此外，我们还说明了如何在具有挑战性的真实世界问题上成功地将在线运动推广到动态环境中。
Motion can be described in alternative represen- tations, including joint configuration or end-effector spaces, but also more complex topological representations that imply a change of Voronoi bias, metric or topology of the motion space. Certain types of robot interaction problems, e.g. wrapping around an object, can suitably be described by so-called writhe and interaction mesh representations. However, considering mo- tion synthesis solely in topological spaces is insufficient since it does not cater for additional tasks and constraints in other representations. In this paper we propose methods to combine and exploit different representations for motion synthesis, with specific emphasis on generalization of motion to novel situations. Our approach is formulated in the framework of optimal con- trol as an approximate inference problem, which allows for a direct extension of the graphical model to incorporate multiple representations. Motion generalization is similarly performed by projecting motion from topological to joint configuration space. We demonstrate the benefits of our methods on problems where direct path finding in joint configuration space is extremely hard whereas local optimal control exploiting a representation with different topology can efficiently find optimal trajectories. Further, we illustrate the successful online motion generalization to dynamic environments on challenging, real world problems.