Footstep Planning for Slippery and Slanted Terrain Using Human-Inspired Models
Footstep Planning for Slippery and Slanted Terrain Using Human-Inspired Models
复制标题
DOI：
10.1109/tro.2016.2581219
复制
发表时间：
2016-07
影响因子：
7.8
通讯作者：
Martim Brandao;K. Hashimoto;J. Santos-Victor;A. Takanishi
中科院分区：
文献类型：
--
作者：
Martim Brandao;K. Hashimoto;J. Santos-Victor;A. Takanishi
对于现实世界中部署的人形机器人来说，能源效率和不同地形条件下运动的鲁棒性是重要的问题。在本文中，我们提出了一种适用于平坦、倾斜和湿滑地形的人形足迹规划算法，该算法使用从人类步态文献中收集的简单原理和表示。规划器使用混合 A* 搜索和优化方法，根据运动可行性和地面摩擦力约束来优化质心 (COM) 机械功模型。足迹的位置和方向是使用 A* 算法搜索的离散状态，而其他相关参数是通过状态转换的持续优化来计算的。这些参数也受到人类步态文献的启发，包括脚步计时（双支撑和摆动时间）和使用膝关节弯曲角度关键点的参数化 COM 运动。规划器依赖于功、所需的摩擦系数 (RCOF) 以及我们在物理模拟中估计的可行性模型。我们通过模拟实验表明，所提出的规划器可以在各种场景下实现低电能消耗和类似人类的运动。使用规划器，机器人根据湿滑区域的大小和摩擦力自动选择避开或（缓慢）穿过湿滑区域，并选择能量最佳的楼梯和斜坡的攀爬角度。获得的运动也与人类步态文献中发现的观察结果一致，例如在湿滑地形上类似人类的RCOF、步长和双支撑时间的变化，以及在陡坡上类似人类的曲线行走。最后，我们将 COM 工作最小化与目标函数的其他选择进行比较。
Energy efficiency and robustness of locomotion to different terrain conditions are important problems for humanoid robots deployed in the real world. In this paper, we propose a footstep-planning algorithm for humanoids that is applicable to flat, slanted, and slippery terrain, which uses simple principles and representations gathered from human gait literature. The planner optimizes a center-of-mass (COM) mechanical work model subject to motion feasibility and ground friction constraints using a hybrid A* search and optimization approach. Footstep placements and orientations are discrete states searched with an A* algorithm, while other relevant parameters are computed through continuous optimization on state transitions. These parameters are also inspired by human gait literature and include footstep timing (double-support and swing time) and parameterized COM motion using knee flexion angle keypoints. The planner relies on work, the required coefficient of friction (RCOF), and feasibility models that we estimate in a physics simulation. We show through simulation experiments that the proposed planner leads to both low electrical energy consumption and human-like motion on a variety of scenarios. Using the planner, the robot automatically opts between avoiding or (slowly) traversing slippery patches depending on their size and friction, and it chooses energy-optimal stairs and climbing angles in slopes. The obtained motion is also consistent with observations found in human gait literature, such as human-like changes in RCOF, step length and double-support time on slippery terrain, and human-like curved walking on steep slopes. Finally, we compare COM work minimization with other choices of the objective function.