发表机构
Indian Institute of Technology Mandi; Clemson University（印度理工学院曼迪分校; 克莱姆森大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于密度的分层MPC框架，使多台四足机器人仅凭初始和目标位姿协作推动物体，实现避障与目标收敛，并在MuJoCo中验证其优于基线方法。
AI 中文摘要
本文提出了一种基于密度的分层模型预测控制框架，用于多台四足机器人的安全协作操作。该框架使机器人团队仅利用初始和目标位姿即可将共享物体推至期望位姿，无需预先计算参考轨迹。中央箱级MPC优化接触力，同时强制执行控制密度约束以实现目标收敛和避障。然后，每个机器人在所述共享信息假设下，求解自己的分布式机器人级全身MPC，以跟踪其移动接触位置，同时考虑静态障碍物和相邻机器人的时变位置。该方法在MuJoCo中使用全身接触动力学进行评估，涉及两台和三台Unitree Go2四足机器人协作推动刚性物体通过狭窄通道。与匹配的控制屏障函数和基于RRT*的跟踪基线的比较表明，所提出的基于密度的公式在纯推动、力和力矩耦合操作任务中的有效性。实现视频可在该https URL获取。
英文摘要
This paper presents a hierarchical density-based model predictive control framework for safe collaborative manipulation by multiple quadrupedal robots. The framework enables a team of robots to push a shared object to a desired pose using only the initial and goal poses, without requiring a precomputed reference trajectory. A centralized box-level MPC optimizes contact forces while enforcing a control-density constraint for goal convergence and obstacle avoidance. Each robot then solves its own distributed robot-level whole-body MPC, under a stated shared-information assumption, to track its moving contact location while accounting for static obstacles and the time-varying positions of neighboring robots. The approach is evaluated in MuJoCo using whole-body contact dynamics for two and three Unitree Go2 quadrupeds collaboratively pushing rigid objects through narrow passages. Comparisons with matched Control Barrier Function and RRT* based tracking baselines demonstrate the effectiveness of the proposed density-based formulation for push-only, force- and torque-coupled manipulation tasks. Implementation videos are available at https://jaggu2606.github.io/go2-density-mpc-pushing/
