Safety-Critical Centralized Nonlinear MPC for Cooperative Payload Transportation by Two Quadrupedal Robots
2026-04-03 • Robotics
Robotics
AI summaryⓘ
The authors developed a control method to help two four-legged robots carry a heavy object together without crashing into obstacles. They created a mathematical model that links the robots and the object to understand how they move and interact. Their system uses special safety rules to avoid collisions in tricky environments and adjusts in real time to disturbances like pushes or uncertain weights. The authors tested their method on actual robots moving through cluttered areas and showed it works well.
nonlinear model predictive controlquadrupedal robotspayload transportationdifferential-algebraic equationscontrol barrier functionscollision avoidanceinteraction wrenchesreal-time controlrobot dynamicsoptimal control problem
Authors
Ruturaj S. Sambhus, Yicheng Zeng, Kapi Ketan Mehta, Jeeseop Kim, Kaveh Akbari Hamed
Abstract
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.