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.

翻译：尽管大语言模型具备强大的语义理解与代码生成能力，但在处理复杂任务时仍面临挑战。多智能体策略生成与运动控制作为高度复杂的领域，本质需要多领域专家协同合作。为增强多智能体策略生成与运动控制能力，我们提出一种创新架构，采用云边端层级结构理念。通过利用多个具有不同专业领域知识的大语言模型，可高效生成策略并实现任务分解。引入余弦相似度方法，在向量层面将任务分解指令与机器人任务序列对齐，能够识别存在未完全分解的子任务，通过多轮迭代最终生成可执行的机器任务序列。机器人依循这些任务序列完成更高复杂度的任务。基于该架构，我们实现了自然语言控制机器人执行复杂任务的全流程，并成功解决了开放场景下多智能体执行开放任务以及任务分解的难点问题。