让机器人在不平地面稳健行走，靠实时感知与智能落脚规划。
Perceptive Mixed-Integer Footstep Control for Underactuated Bipedal Walking on Rough Terrain
- 用混合整数规划统一优化落脚点、步态和关节力矩，每秒100次以上。
- 在线生成凸多边形地形分解，实现实时安全区域识别。
- 适用于足式机器人在复杂地形自主行走，适合研究者参考。
穿越崎岖地形需要动态双足机器人通过合理选择落脚点来保持稳定，同时避免进入危险区域。由于安全地形具有非凸性，且感知与状态估计存在误差，实现在线步态规划极具挑战。本文提出一个端到端的感知与控制系统，用于实现欠驱动双足机器人在不连续地形上的行走。首先，设计了模型预测落脚控制（MPFC），将落脚点选择、步位、踝部力矩、模板动力学及步态时机统一为一个混合整数二次规划问题，在超过100 Hz的频率下求解。其次，提出一种新型在线生成凸多边形地形分解的方法。其感知系统将安全区域分类与平面多边形拟合分离，仅用单个CPU线程即可实现实时、时序一致的地形分割。通过在欠驱动双足机器人Cassie上进行户外实验，验证了该系统在不连续地形上实现了当前最先进的感知式双足行走性能。补充视频：https://youtu.be/JK16KJXJxi4
原文摘要 · Abstract (English)
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
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