同步估计足部受力与状态，提升机器人在复杂地形的适应能力。
Simultaneous Ground Reaction Force and State Estimation via Constrained Moving Horizon Estimation
- 基于约束移动窗口优化，融合多源传感器与动力学模型。
- 200Hz频率下，0.04秒窗口内实现高精度受力与状态估计。
- 适用于人形、双足和四足机器人，适合实时控制场景。
精确的地面反作用力（GRF）估计能显著提升腿式机器人的实际应用适应性。例如，结合估计的GRF与接触运动学，可辅助运动控制与规划以应对不确定地形。传统基于动量的方法作为非线性观测器，未能充分解决测量噪声及浮点基状态与广义动量动力学之间的耦合问题。本文提出一种针对腿式机器人的同步地面反作用力与状态估计框架，系统性地处理传感器噪声及状态与动力学间的耦合。在独立估计浮点基姿态的基础上，采用分布式移动窗口估计（MHE）方法，在凸窗化优化中融合机器人动力学、本体感知传感器、外部感知传感器以及确定性接触互补约束。所提方法在多个腿式机器人上验证有效，包括自研人形机器人Bucky、开源教育用平面双足机器人STRIDE和四足机器人Unitree Go1，运行频率达200Hz，时间窗口为0.04s。
原文摘要 · Abstract (English)
Accurate ground reaction force (GRF) estimation can significantly improve the adaptability of legged robots in various real-world applications. For instance, with estimated GRF and contact kinematics, the locomotion control and planning assist the robot in overcoming uncertain terrains. The canonical momentum-based methods, formulated as nonlinear observers, do not fully address the noisy measurements and the dependence between floating-base states and the generalized momentum dynamics. In this paper, we present a simultaneous ground reaction force and state estimation framework for legged robots, which systematically addresses the sensor noise and the coupling between states and dynamics. With the floating base orientation estimated separately, a decentralized Moving Horizon Estimation (MHE) method is implemented to fuse the robot dynamics, proprioceptive sensors, exteroceptive sensors, and deterministic contact complementarity constraints in a convex windowed optimization. The proposed method is shown to be capable of providing accurate GRF and state estimation on several legged robots, including the custom-designed humanoid robot Bucky, the open-source educational planar bipedal robot STRIDE, and the quadrupedal robot Unitree Go1, with a frequency of 200Hz and a past time window of 0.04s.
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