Problem
Research questions and friction points this paper is trying to address.
Multi-Robot Collaborative Transport
Safe Manipulation
Quadrupedal Robots
Obstacle Avoidance
Model Predictive Control
Safe Multi-Robot Collaborative Transport Using Density Functions
🤖 AI Summary
This study addresses the challenges of safe control and obstacle avoidance in cooperative transport by multi-legged robots without predefined trajectories. We propose a hierarchical density-based Model Predictive Control (MPC) framework. The upper layer performs centralized optimization of contact forces, incorporating density function constraints to ensure target convergence and global safety. The lower layer employs distributed tracking of contact points to achieve dynamic obstacle avoidance and inter-robot collision prevention. This approach handles pure thrust-coupled tasks without requiring reference trajectories. Validated through whole-body dynamics modeling and MuJoCo simulations, the proposed method significantly outperforms CBF and RRT* baselines in narrow-passage scenarios, effectively enabling safe cooperative manipulation among multi-legged robots.
📝 Abstract
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/
Innovation
Methods, ideas, or system contributions that make the work stand out.
Density Functions
Model Predictive Control
Multi-Robot Collaborative Transport
Quadrupedal Robots
Hierarchical Control
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