  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.
