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

Ibrahim Muhammed1, Ayman A Nada2, Haitham El-Hussieny3

  • 1Department of Mechatronics and Robotics Engineering, Egypt-Japan University of Science and Technology, Alexandria, Egypt. ibrahim.muhammed@ejust.edu.eg.

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|March 23, 2026
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Summary

This study validates a decentralized Model Predictive Control (MPC) framework for multi-robot cooperative transport. The system achieves robust, real-time object transportation with adaptive control, even with dynamic obstacles.

Keywords:
Adaptive controlConstraint handlingCooperative object transportationDecentralized model predictive control (MPC)Multi-robot systemsReal-time controlTrajectory tracking

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Area of Science:

  • Robotics
  • Control Systems
  • Artificial Intelligence

Background:

  • Cooperative object transportation with multiple robots presents challenges in coordination and constraint satisfaction.
  • Decentralized control offers scalability but requires effective inter-robot communication and coordination strategies.
  • Model Predictive Control (MPC) is a powerful framework for handling constraints and optimizing system behavior.

Purpose of the Study:

  • To experimentally validate a decentralized Model Predictive Control (MPC) framework for cooperative object transportation using two mobile robots.
  • To demonstrate the framework's ability to handle nonlinear kinematics, joint dynamics, inter-robot constraints, and dynamic obstacle avoidance in real-time.
  • To assess the effectiveness of adaptive weighting for balancing trajectory tracking and formation objectives.

Main Methods:

  • Development of a decentralized MPC framework with joint-space coupling for global coordination.
  • Inclusion of nonlinear kinematics, revolute-prismatic joint dynamics, and inter-robot constraints in the optimization.
  • Implementation of dynamic obstacle avoidance and adaptive weighting of cost terms.
  • Deployment on a physical testbed with vision-based pose estimation, Kalman filter sensor fusion, and ROS 2 infrastructure.

Main Results:

  • Accurate trajectory tracking achieved across point-to-point, curvilinear, and obstacle-rich scenarios.
  • Strict satisfaction of all defined constraints, including inter-robot and obstacle avoidance.
  • Demonstrated robustness to environmental uncertainties and dynamic changes.
  • Successful cooperative object transportation validated through physical experiments.

Conclusions:

  • Decentralized constrained MPC with adaptive weights is a practical and scalable solution for real-time multi-robot cooperative transport.
  • The proposed framework effectively manages complex dynamics and constraints in cooperative robotic tasks.
  • Experimental validation confirms the framework's performance and robustness in realistic scenarios.