Bipedal robots promise the ability to traverse rough terrain quickly and efficiently, and indeed, humanoid robots can now use strong ankles and careful foot placement to traverse discontinuous terrain. However, more agile underactuated bipeds have small feet and weak ankles, and must constantly adjust their planned footstep position to maintain balance. We introduce a new model-predictive footstep controller which jointly optimizes over the robot's discrete choice of stepping surface, impending footstep position sequence, ankle torque in the sagittal plane, and center of mass trajectory, to track a velocity command. The controller is formulated as a single Mixed Integer Quadratic Program (MIQP) which is solved at 50-200 Hz, depending on terrain complexity. We implement a state of the art real-time elevation mapping and convex terrain decomposition framework to inform the controller of its surroundings in the form on convex polygons representing steppable terrain. We investigate the capabilities and challenges of our approach through hardware experiments on the underactuated biped Cassie.

翻译：双足机器人承诺能够快速高效地穿越崎岖地形，事实上，人形机器人现已能利用强壮的踝关节和精确的落脚点来穿越不连续地形。然而，更敏捷的欠驱动双足机器人脚掌较小、踝关节较弱，必须不断调整预定的落脚点位置以维持平衡。我们提出了一种新型模型预测脚步控制器，该控制器联合优化机器人的离散落脚面选择、即将执行的落脚点序列、矢状面踝关节力矩及质心轨迹，以追踪速度指令。该控制器被表述为一个混合整数二次规划（MIQP）问题，根据地形复杂度以50-200 Hz的频率求解。我们实现了一种基于实时高程映射与凸地形分解的先进框架，以可落脚地形对应的凸多边形形式向控制器提供环境信息。通过在欠驱动双足机器人Cassie上的硬件实验，我们探讨了该方法的潜力与挑战。