Humanoids’ abilities to navigate stairs and uneven terrain make them well-suited for disaster response efforts. However, humanoid navigation in such environments is currently limited by the capabilities of navigation planners. Such planners typically consider only footstep locations, but planning with palm contacts may be necessary to cross a gap, avoid an obstacle, or maintain balance. However, considering palm contacts greatly increases the branching factor of the search, leading to impractical planning times for large environments. Planning a contact transition sequence in a large environment is important because it verifies that the robot will be able to reach a given goal. In previous work we explored using library-based methods to address difficult navigation planning problems requiring palm contacts, but such methods are not efficient when navigating an easy-to-traverse part of the environment. To maximize planning efficiency, we would like to use discrete planners when an area is easy to traverse and switch to the library-based method only when traversal becomes difficult. Thus, in this paper we present a method that (1) Plans a torso guiding path which accounts for the difficulty of traversing the environment as predicted by learned regressors; and (2) Decomposes the guiding path into a set of segments, each of which is assigned a motion mode (i.e. a set of feet and hands to use) and a planning method. Easily-traversable segments are assigned a discrete-search planner, while other segments are assigned a library-based method that fits existing motion plans to the environment near the given segment. Our results suggest that the proposed approach greatly outperforms standard discrete planning in success rate and planning time. We also show an application of the method to a real robot in a mock disaster scenario.

类人机器人在楼梯和不平坦地形上的导航能力使它们非常适合灾难响应工作。然而，这种环境中的人形导航目前受到导航规划者能力的限制。此类规划者通常仅考虑足迹位置，但可能需要使用手掌接触进行规划以跨越间隙、避开障碍物或保持平衡。然而，考虑手掌接触会大大增加搜索的分支因素，导致大型环境的规划时间不切实际。在大环境中规划接触转换序列很重要，因为它可以验证机器人是否能够达到给定的目标。在之前的工作中，我们探索了使用基于库的方法来解决需要手掌接触的困难导航规划问题，但是在导航环境中易于穿越的部分时，此类方法效率不高。为了最大限度地提高规划效率，我们希望在区域易于遍历时使用离散规划器，仅在遍历变得困难时才切换到基于库的方法。因此，在本文中，我们提出了一种方法：（1）规划一条躯干引导路径，该路径解释了学习回归器预测的穿越环境的难度；(2)将引导路径分解为一组段，每个段被分配一种运动模式（即一组要使用的脚和手）和一种规划方法。容易遍历的段被分配了一个离散搜索规划器，而其他段被分配了一个基于库的方法，使现有的运动计划适合给定段附近的环境。我们的结果表明，所提出的方法在成功率和规划时间方面大大优于标准离散规划。我们还在模拟灾难场景中展示了该方法在真实机器人上的应用。