基于MPC步态控制的有约束落脚点双足行走
Bipedal Walking on Constrained Footholds with MPC Footstep Control
AI总结：
提出一种基于MIQP的模型预测步态控制器，联合优化落脚面选择、落脚位置、踝关节扭矩和质心轨迹，使欠驱动双足机器人Cassie能在约束地形上保持平衡行走。
摘要：
双足机器人有望快速高效地穿越崎岖地形，事实上，人形机器人现在可以利用强壮的脚踝和谨慎的落脚来穿越不连续地形。然而，更敏捷的欠驱动双足机器人具有小脚掌和弱脚踝，必须不断调整其规划的落脚位置以保持平衡。我们引入了一种新的模型预测步态控制器，该控制器联合优化机器人对踩踏表面的离散选择、即将到来的落脚位置序列、矢状面内的脚踝扭矩以及质心轨迹，以跟踪速度指令。该控制器被表述为单个混合整数二次规划（MIQP），根据地形复杂度以50-200 Hz的频率求解。我们实现了最先进的实时高程映射和凸地形分解框架，以表示可踩踏地形的凸多边形形式向控制器提供周围环境信息。我们通过在欠驱动双足机器人Cassie上的硬件实验研究了该方法的能力与挑战。
英文摘要：
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.
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。
