ADMM-Based Safety-Critical Distributed NMPC for Cooperative Transportation by Quadrupedal Robots
Robotics
Summary
The authors developed a control method that helps teams of four-legged robots safely carry heavy objects together. They treat the robots and the object as a connected system and break a big planning problem into smaller parts so each robot can work in parallel, while still coordinating well. Their method uses special math tools to avoid collisions and keep the robots moving smoothly, even with delays or disturbances. Tests in simulations and real-world experiments show their approach is faster than a centralized method and keeps the object stable during transport.
Authors
Ruturaj S. Sambhus, Kapi Ketan Mehta, Yicheng Zeng, Kaveh Akbari Hamed
Abstract
This paper presents a safety-critical distributed nonlinear model predictive control (DNMPC) framework for cooperative payload transportation by teams of quadrupedal robots. The proposed approach models the robotic team and the shared payload as a dynamically coupled networked system with rigid holonomic coupling constraints arising from cooperative transportation. To enable distributed real-time optimization, the centralized finite-horizon optimal control problem is decomposed into parallel local NMPC subproblems coordinated through the alternating direction method of multipliers (ADMM). The resulting distributed framework enforces consensus over both payload-state and interaction-wrench trajectories while explicitly incorporating acceleration-level holonomic coupling constraints within the distributed predictive control formulation. Safety-critical obstacle avoidance constraints for both the robotic agents and payload are enforced using higher-order control barrier functions (HOCBFs). The framework is validated through numerical simulations with teams of two, three, and four quadrupedal robots transporting shared payloads in cluttered environments. Real-time experiments on two- and three-robot teams demonstrate safe and robust transportation under payload uncertainty and external disturbances. Compared with centralized NMPC, the proposed framework achieves up to 23% reduction in average NLP solve time while maintaining comparable closed-loop performance. Ablation studies further demonstrate robustness to communication delays and show that explicit payload-state consensus and holonomic constraints substantially improve payload tracking and distributed coordination over existing wrench-only consensus formulations.