Effective Footstep Planning for Humanoids Using Homotopy-Class Guidance
Effective Footstep Planning for Humanoids Using Homotopy-Class Guidance
复制标题
使用同伦类指导对人形机器人进行有效的足迹规划
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Maxim Likhachev
中科院分区：
文献类型：
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作者：
Vinitha Ranganeni;Oren Salzman;Maxim Likhachev
由于系统的高维性，规划人形机器人的运动是一项计算复杂的任务。因此，一种常见的方法是首先在机器人脚引起的低维空间中进行规划——这一任务称为足迹规划。然后使用这个低维计划来引导机器人的完整运动。在足迹规划中已被证明成功的一种方法是使用基于搜索的规划器，例如 A* 及其许多变体。为此，这些基于搜索的规划器必须被赋予有效的启发式方法，以有效地引导他们通过搜索空间。然而，设计有效的启发式方法是一项耗时的任务，需要用户具备良好的领域知识。因此，我们的目标是能够有效地规划人形机器人采取的脚步运动，同时消除用户仔细设计局部最小值自由启发式的负担。为此，我们建议在工作区中使用用户定义的同伦类，这些类的定义很直观。这些同伦类用于自动生成启发式函数，有效指导足迹规划器。我们将足迹规划方法与使用足迹规划通用启发式的标准方法进行比较。在简单场景下，两种算法的性能相当。然而，在更复杂的场景中，与标准方法相比，我们的方法可以将规划速度提高几个数量级。
Planning the motion for humanoid robots is a computationally-complex task due to the high dimensionality of the system. Thus, a common approach is to first plan in the low-dimensional space induced by the robot’s feet—a task referred to as footstep planning. This low-dimensional plan is then used to guide the full motion of the robot. One approach that has proven successful in footstep planning is using search-based planners such as A* and its many variants. To do so, these search-based planners have to be endowed with effective heuristics to efficiently guide them through the search space. However, designing effective heuristics is a time-consuming task that requires the user to have good domain knowledge. Thus, our goal is to be able to effectively plan the footstep motions taken by a humanoid robot while obviating the burden on the user to carefully design local-minima free heuristics. To this end, we propose to use user-defined homotopy classes in the workspace that are intuitive to define. These homotopy classes are used to automatically generate heuristic functions that efficiently guide the footstep planner. We compare our approach for footstep planning with a standard approach that uses a heuristic common to footstep planning. In simple scenarios, the performance of both algorithms is comparable. However, in more complex scenarios our approach allows for a speedup in planning of several orders of magnitude when compared to the standard approach.