通过联合优化步态相位时长提升人形机器人行走稳定性
Phase-based Nonlinear Model Predictive Control for Humanoid Walking Stabilization with Single and Double Support Time Adjustments
- 基于非线性模型预测控制，统一优化支撑相与双足相持续时间
- 在外部干扰下仍保持平衡，实测性能优于传统方法
- 适合研究人形机器人动态步态控制的学者与工程师
人形机器人行走中的接触序列由单足支撑相（SSP）和双足支撑相（DSP）组成，其时长协调与状态驱动的动态过渡对维持行走稳定性至关重要。已有大量研究证明，相位时长优化是提升稳定性的重要手段。本文提出一种基于相位的非线性模型预测控制（NMPC）框架，将零力矩点（ZMP）调制、步位调整、单足支撑相持续时间（步态时序）与双足支撑相持续时间在同一公式中联合优化。具体而言，该框架将非线性发散运动分量（DCM）误差动力学重构为相位一致表示，并作为动态约束嵌入NMPC求解器。所提方法在接触相切换时保证ZMP输入连续性，并在双足支撑相禁用步位更新，从而实现无论处于单足或双足支撑阶段均具备动态可靠性。通过大量仿真与硬件实验验证，该方法在外部扰动下表现出更优的平衡性能。
原文摘要 · Abstract (English)
The contact sequence of humanoid walking consists of single and double support phases (SSP and DSP), and their coordination through proper duration and dynamic transition based on the robot's state is crucial for maintaining walking stability. Numerous studies have investigated phase duration optimization as an effective means of improving walking stability. This paper presents a phase-based Nonlinear Model Predictive Control (NMPC) framework that jointly optimizes Zero Moment Point (ZMP) modulation, step location, SSP duration (step timing), and DSP duration within a single formulation. Specifically, the proposed framework reformulates the nonlinear DCM (Divergent Component of Motion) error dynamics into a phase-consistent representation and incorporates them as dynamic constraints within the NMPC. The proposed framework also guarantees ZMP input continuity during contact-phase transitions and disables footstep updates during the DSP, thereby enabling dynamically reliable balancing control regardless of whether the robot is in SSP or DSP. The effectiveness of the proposed method is validated through extensive simulation and hardware experiments, demonstrating improved balance performance under external disturbances.
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