发表机构
Virginia Tech（弗吉尼亚理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究四足机器人协同运输的安全关键型分布式控制框架，核心方法是用ADMM分解优化问题，结合高阶控制障碍函数。相比集中式NMPC，减少求解时间，保持性能，消融研究显示对通信延迟鲁棒，能改善负载跟踪与协调。
AI 中文摘要
本文提出了一种用于四足机器人团队协同负载运输的安全关键型分布式非线性模型预测控制（DNMPC）框架。该方法将机器人团队和共享负载建模为具有协同运输产生的刚性完整耦合约束的动态耦合网络系统。通过交替方向乘子法（ADMM）将集中式有限时域最优控制问题分解为并行的局部NMPC子问题。该分布式框架在分布式预测控制公式中明确纳入加速度级完整耦合约束的同时，强制实现负载状态和相互作用扳手轨迹的一致性。使用高阶控制障碍函数（HOCBFs）来实施机器人代理和负载的安全关键避障约束。通过数值模拟和实时实验验证了该框架的有效性。与集中式NMPC相比，该框架在保持可比闭环性能的同时，平均NLP求解时间最多减少23%。消融研究进一步证明了对通信延迟的鲁棒性，并表明明确的负载状态一致性和完整约束显著改善了负载跟踪和分布式协调。
英文摘要
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.
CommentsSupplementary video available at: https://youtu.be/w8hg52T8Luc?si=nzQrGsqP5ZBNFlGp
