ADMM-Based Safety-Critical Distributed NMPC for Cooperative Transportation by Quadrupedal Robots

2026-07-18

AI Summary

This work addresses the challenges of safety and real-time control in cooperative multi-legged robotic transport by proposing a distributed nonlinear model predictive control (NMPC) framework. The robot–payload system is modeled as a dynamically coupled network, and the centralized optimization problem is decomposed into parallel subproblems via the alternating direction method of multipliers (ADMM). Within this distributed architecture, payload state consensus and interaction torque coordination are simultaneously achieved, with acceleration-level rigid holonomic constraints explicitly embedded for the first time. High-order control barrier functions (HOCBFs) are integrated to ensure dual obstacle avoidance for both robots and the payload. Simulations and physical experiments demonstrate that, compared to centralized NMPC, the proposed approach reduces average computation time by 23% while exhibiting superior safety, coordination accuracy, and robustness under communication delays and external disturbances.

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.

Problem

• cooperative transportation
• quadrupedal robots
• safety-critical control
• distributed NMPC
• holonomic constraints

Innovation

• ADMM
• distributed NMPC
• holonomic coupling constraints
• higher-order control barrier functions
• quadrupedal robot cooperation