两足机器人协作运载，用非线性预测控制保障安全
Safety-Critical Centralized Nonlinear MPC for Cooperative Payload Transportation by Two Quadrupedal Robots
- 基于约束的非线性模型预测控制，融合碰撞规避与交互力优化
- 在杂乱环境和扰动下成功实现双机器人协同运载，实测稳定可靠
- 适合需要高安全性协作的复杂场景，如救援或工业搬运
本文提出一种面向双足机器人协作运载的安全部署式非线性模型预测控制（NMPC）框架。将机器人-负载系统建模为离散时间非线性微分代数系统，通过运动学约束和交互作用力捕捉耦合动力学。为确保复杂环境中的安全性，设计基于控制屏障函数（CBF）的NMPC方法，强制实施对机器人与负载的避障约束。所提方法将交互作用力作为决策变量，形成结构化微分代数方程（DAE）约束最优控制问题，支持高效实时求解。算法在两个Unitree Go2平台上的硬件实验中得到验证，可在存在质量与惯性不确定性及外部推力干扰的杂乱环境中完成协作运载任务。
原文摘要 · Abstract (English)
This paper presents a safety-critical centralized nonlinear model predictive control (NMPC) framework for cooperative payload transportation by two quadrupedal robots. The interconnected robot-payload system is modeled as a discrete-time nonlinear differential-algebraic system, capturing the coupled dynamics through holonomic constraints and interaction wrenches. To ensure safety in complex environments, we develop a control barrier function (CBF)-based NMPC formulation that enforces collision avoidance constraints for both the robots and the payload. The proposed approach retains the interaction wrenches as decision variables, resulting in a structured DAE-constrained optimal control problem that enables efficient real-time implementation. The effectiveness of the algorithm is validated through extensive hardware experiments on two Unitree Go2 platforms performing cooperative payload transportation in cluttered environments under mass and inertia uncertainty and external push disturbances.
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