摘要： 在家庭服务场景中，多机器人系统需处理非专业用户发出的自然语言指令，这对自动化任务调度提出更高要求。针对现有多机器人调度方法在任务理解、依赖管理和资源优化方面的不足，本研究提出DAG-LLM调度方法，实现从自然语言输入到多机协作的全流程自动化。该方法首先，利用大语言模型（LLM）结合环境信息进行语义解析与任务分解，通过抽象链（CoA）机制生成具备执行约束的子任务集合；其次，基于LLM自动构建子任务间的有向无环图（DAG），取代传统人工建模流程，准确表征任务依赖关系；最后采用回溯算法匹配机器人技能与子任务需求，结合异步执行策略提升执行，在保证依赖顺序前提下通过动态调度减少等待时间。为验证方法有效性，在AI2-THOR仿真环境中设计三类不同复杂度的家庭任务（含4组场景）开展对比实验。实验数据显示，DAG-LLM在任务成功率上相较SMART-LLM提升43.3%，相较AutoTAMP提升60.0%；运行时间分别缩短32.8%和39.4%。消融实验进一步表明任务依赖建模和异步执行机制对提升系统性能具有关键作用。该方法无需人工参与任务分解与依赖建模，适用于多机器人智能体在家庭等自然语言驱动的应用场景下的高效协作调度。

Abstract: In the home service scenario, multi robot systems need to process natural language instructions issued by non professional users, which poses higher requirements for automated task scheduling. In response to the shortcomings of existing multi robot scheduling methods in task understanding, dependency management, and resource optimization, this study proposes the DAG-LLM scheduling method to achieve full process automation from natural language input to multi machine collaboration. This method first utilizes the Large Language Model (LLM) combined with environmental information for semantic parsing and task decomposition, and generates a set of subtasks with execution constraints through the Chain-of-Abstraction (CoA) mechanism; Secondly, based on LLM, a Directed Acyclic Graph (DAG) is automatically constructed between subtasks to replace traditional manual modeling processes and accurately represent task dependencies; Finally, the backtracking algorithm is used to match robot skills with subtask requirements, combined with asynchronous execution strategy to improve execution, and dynamic scheduling is used to reduce waiting time while ensuring dependency order. To verify the effectiveness of the method, three types of household tasks with different complexities (including four sets of scenarios) were designed in the AI2-THOR simulation environment for comparative experiments. Experimental data show that DAG-LLM improves the task success rate by 43.3% compared with SMART-LLM and 60.0% compared with AutoTAMP; The running time is shortened by 32.8% and 39.4% respectively. The ablation experiment further demonstrates that task dependency modeling and asynchronous execution mechanisms play a crucial role in improving system performance. This method does not require manual involvement in task decomposition and dependency modeling, and is suitable for efficient collaborative scheduling of multi robot intelligent agents in natural language driven application scenarios such as homes.