面向崎岖地形欠驱动双足行走的感知型混合整数脚步控制
Perceptive Mixed-Integer Footstep Control for Underactuated Bipedal Walking on Rough Terrain
AI总结：
本文提出全栈感知与MPFC混合整数二次规划控制系统，结合在线凸多边形地形分解，使Cassie能在不连续崎岖地形上以超100 Hz实现安全、鲁棒的欠驱动双足行走。
摘要：
穿越崎岖地形要求动态双足机器人通过落脚点放置来稳定自身，同时不能踩入不安全区域。鉴于安全地形的非凸性以及不完美的感知和状态估计，在线规划这些脚步具有挑战性。本文通过一套用于在不连续地形上实现欠驱动行走的全栈感知与控制系统来应对这些挑战。首先，我们开发了模型预测脚步控制（MPFC），这是一个单一的混合整数二次规划，假设采用凸多边形地形分解，以超过100 Hz的频率对离散立足点选择、脚步位置、踝关节力矩、模板动力学和脚步时机进行优化。随后，我们提出了一种在线生成凸多边形地形分解的新方法。我们的感知栈将安全地形分类与平面多边形拟合解耦，仅使用单个CPU线程即可实时生成时间一致的地形分割。我们通过欠驱动双足机器人Cassie的户外实验展示了感知与控制栈的性能，在不连续地形上实现了最先进的感知型双足行走。补充视频：https://youtu.be/JK16KJXJxi4
英文摘要：
Traversing rough terrain requires dynamic bipeds to stabilize themselves through foot placement without stepping in unsafe areas. Planning these footsteps online is challenging given non-convexity of the safe terrain, and imperfect perception and state estimation. This paper addresses these challenges with a full-stack perception and control system for achieving underactuated walking on discontinuous terrain. First, we develop model-predictive footstep control (MPFC), a single mixed-integer quadratic program which assumes a convex polygon terrain decomposition to optimize over discrete foothold choice, footstep position, ankle torque, template dynamics, and footstep timing at over 100 Hz. We then propose a novel approach for generating convex polygon terrain decompositions online. Our perception stack decouples safe-terrain classification from fitting planar polygons, generating a temporally consistent terrain segmentation in real time using a single CPU thread. We demonstrate the performance of our perception and control stack through outdoor experiments with the underactuated biped Cassie, achieving state of the art perceptive bipedal walking on discontinuous terrain. Supplemental Video: https://youtu.be/JK16KJXJxi4
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。
