Hierarchical Large Language Models in Cloud-Edge-End Architecture for Heterogeneous Robot Cluster Control

Despite their powerful semantic understanding and code generation capabilities, Large Language Models (LLMs) still face challenges when dealing with complex tasks. Multi-agent strategy generation and motion control are highly complex domains that inherently require experts from multiple fields to collaborate. To enhance multi-agent strategy generation and motion control, we propose an innovative architecture that employs the concept of a cloud-edge-end hierarchical structure. By leveraging multiple large language models with distinct areas of expertise, we can efficiently generate strategies and perform task decomposition. Introducing the cosine similarity approach, aligning task decomposition instructions with robot task sequences at the vector level, we can identify subtasks with incomplete task decomposition and iterate on them multiple times to ultimately generate executable machine task sequences.The robot is guided through these task sequences to complete tasks of higher complexity. With this architecture, we implement the process of natural language control of robots to perform complex tasks, and successfully address the challenge of multi-agent execution of open tasks in open scenarios and the problem of task decomposition.