Safe Multi-Robot Collaborative Transport Using Density Functions
Authors: Jagannath Prasad Sahoo, Sriram S. K. S. Narayanan, Umesh Vaidya
Organizations: Center for Artificial Intelligence and Robotics, Indian Institute of Technology Mandi, India · Department of Mechanical Engineering, Clemson University, Clemson, SC 29630, USA
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/
Figures & tables
Fig. 1 : Hierarchical architecture of the proposed framework with box-level density MPC and robot-level density MPC.
Fig. 2 : Panels (a) and (b) show the density and corresponding navigation field for a circular region, (c) and (d) show the corresponding construction for a rectangular region.
Parameter
Value
Object-Level Density MPC (box)
Prediction horizon Nb
15
Discretization interval Ts
0.05 s ( 20 Hz)
Box–ground friction μ
0.5
Contact-force bounds [fc,min,fc,max]
[0,45] N
Density exponent α
1.0
TABLE I : MPC parameters for object-level and robot-level controllers.
Fig. 3 : Collaborative manipulation using the proposed density MPC framework evaluated in MuJoCo, from the initial configuration (A) to the goal configuration (E).
Fig. 4 : Comparison of Density MPC, CBF MPC, and RRT with tracking for box (left) and T-shaped object (right) pushing: (a,b) object trajectories; (c,d) distance to goal; (e,f) minimum object–obstacle clearance.
Shenzhen International Graduate School, Tsinghua University, Shenzhen, China. · Department of Electronic Engineering, and the Institute for Embodied Intelligence and Robotics, Tsinghua University, Beijing, China.