Real-time decentralized model predictive control for cooperative multi-robot object transport: experimental validation

TL;DR

This paper validates a decentralized control system for two robots working together to transport objects in real-time, even in complex environments.

Contribution

The paper introduces a decentralized MPC framework with adaptive weighting for real-time multi-robot cooperative transport.

Findings

The framework achieves accurate trajectory tracking and constraint satisfaction in various scenarios.

It demonstrates robustness to environmental uncertainties and dynamic obstacles.

The system scales well for cooperative transport tasks with arbitrary reference paths.

Abstract

This paper presents an experimental validation of a decentralized Model Predictive Control (MPC) framework for cooperative object transportation utilizing a multi-robot system consisting of two mobile robots. Each robot is a differential-drive robot that independently solves local constrained optimization problems while ensuring global coordination through joint-space coupling. The formulation explicitly captures nonlinear kinematics, revolute-prismatic joint dynamics, inter-robot constraints, and dynamic obstacle avoidance within a real-time optimization setting. Adaptive weighting of cost terms is employed to balance trajectory tracking and formation objectives under varying task demands. The framework is deployed on a physical testbed integrating vision-based pose estimation, sensor fusion via a Kalman filter, and a ROS 2 control infrastructure. Experiments across point-to-point, curvilinear, and obstacle-rich scenarios show accurate trajectory tracking, strict constraint satisfaction, and robustness to environmental uncertainties. These results substantiate decentralized constrained MPC with adaptive weights as a practical and scalable solution for real-time multi-robot cooperative transport along arbitrary reference paths.

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