运动规划资源管理器：使用局部极小值树可视化局部极小值
Motion Planning Explorer: Visualizing Local Minima using a Local-Minima Tree
摘要：
运动规划问题往往有许多局部最小值。这些最小值对于可视化以让用户引导、防止或预测运动是重要的。为了实现这一目标，我们开发的运动规划资源管理器，一种算法，让用户交互式地探索一棵树的局部极小值。根据Morse理论的思想，我们将局部极小定义为在最小化成本泛函下的路径不变。局部最小值使用由用户指定的低维投影被分组到局部最小值树中。然后，用户可以交互式地探索局部最小值树，从而可视化问题结构并引导或防止运动。我们展示了运动规划资源管理器忠实地捕捉本地最小值在四个现实的情况下，完整和某些非完整的机器人。
英文摘要：
Motion planning problems often have many local minima. Those minima are important to visualize to let a user guide, prevent or predict motions. Towards this goal, we develop the motion planning explorer, an algorithm to let users interactively explore a tree of local-minima. Following ideas from Morse theory, we define local minima as paths invariant under minimization of a cost functional. The local-minima are grouped into a local-minima tree using lower-dimensional projections specified by a user. The user can then interactively explore the local-minima tree, thereby visualizing the problem structure and guide or prevent motions. We show the motion planning explorer to faithfully capture local minima in four realistic scenarios, both for holonomic and certain non-holonomic robots.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
