安全关键性双六足机器人协同载荷运输的集中非线性MPC
Safety-Critical Centralized Nonlinear MPC for Cooperative Payload Transportation by Two Quadrupedal Robots
AI总结：
本文提出一种安全关键性集中非线性MPC框架，用于双六足机器人协同运输载荷。通过holonomic约束和交互力偶捕捉耦合动力学，采用CBF方法确保安全避障，通过实验验证有效性。
摘要：
本文提出了一种安全关键性的集中非线性模型预测控制（NMPC）框架，用于双六足机器人协同运输载荷。互联的机器人-载荷系统被建模为离散时间非线性微分代数系统，通过holonomic约束和交互力偶捕捉耦合动力学。为确保复杂环境中的安全性，我们开发了一种基于控制屏障函数（CBF）的NMPC方法，强制对机器人和载荷实施碰撞避免约束。所提出的方法将交互力偶作为决策变量，导致一个结构化的DAE约束最优控制问题，从而实现高效的实时实现。该算法的有效性通过在两个Unitree Go2平台上进行的大量硬件实验得到验证，这些实验涉及在存在质量和惯性不确定性和外部推搡干扰的复杂环境中进行协同载荷运输。
英文摘要：
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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