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This paper introduces a centralized multi-robot system (MRS) architecture leveraging a Large Language Model (LLM) to improve task allocation and execution for cooperative object transportation. The system uses prompt engineering to translate natural language instructions into hierarchical JSON commands for task distribution among robots within a ROS-based framework. Experimental results in both simulation and real-world scenarios demonstrate the effectiveness of the LLM-driven MRS control method for coordinated multi-robot navigation and object transportation.
Forget hand-engineered coordination logic: this system uses an LLM to translate natural language instructions into robot commands for cooperative object transportation.
This paper proposes a centralized Large Language Model (LLM)-based Multi-Robot System (MRS) for application of cooperative object transportation. We integrate the LLM into a ROS-based MRS to improve structured task allocation and execution. By leveraging prompt engineering, natural language instructions are converted into hierarchical JSON commands, allowing the central host to efficiently parse and distribute tasks among robots for coordinated multi-robot control. In addition, to achieve efficient and adaptive navigation in different environments, the system integrates $A^{*}$ and Dynamic Window Approach (DWA) algorithms for global path planning and local obstacle avoidance. Simulation and real-world experiments validate the effectiveness of the proposed LLM-based MRS control method. This study contributes to the advancement of MRS in industrial and service applications.
