AutoHMA-LLM: Efficient Task Coordination and Execution in Heterogeneous Multi-Agent Systems Using Hybrid Large Language Models

Heterogeneous multi-agent systems (HMAS) comprise various intelligent agents with specialized functions, such as drones, ground robots, and automated devices, working in coordinated settings. This paper presents AutoHMA-LLM, a novel framework that combines Large Language Models (LLMs) with classical control algorithms to address the challenges of task coordination and scheduling in complex, dynamic environments. The framework is designed with a multi-tier architecture, utilizing a cloud-based LLM as the central planner alongside device-specific LLMs and Generative Agents to improve task execution efficiency and accuracy. Specifically targeting dynamic scenarios, the system enhances resource utilization and stabilizes task execution through refined task scheduling and real-time feedback mechanisms. In experiments conducted across logistics, inspection, and search & rescue scenarios, AutoHMA-LLM demonstrated a 5.7% improvement in task completion accuracy, a 46% reduction in communication steps, and a 31% decrease in token usage and API calls compared to baseline methods. These results highlight our framework’s scalability and efficiency, offering substantial support for effective multi-agent collaboration in complex, resource-constrained environments.