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.

翻译：本文提出了一种面向双四足机器人协同负载运输的安全关键集中式非线性模型预测控制（NMPC）框架。互联的机器人-负载系统被建模为离散时间非线性微分-代数系统，通过完整约束和交互力旋量捕获耦合动力学特性。为确保复杂环境下的安全性，我们开发了基于控制屏障函数（CBF）的NMPC公式，对机器人和负载实施碰撞避免约束。该方法将交互力旋量保留为决策变量，从而形成结构化的DAE约束最优控制问题，支持高效的实时实现。通过在两个Unitree Go2平台上开展的大量硬件实验验证了算法的有效性——在质量与惯性不确定性以及外部推挤干扰下，两机器人于杂乱环境中完成了协同负载运输任务。