用大模型提升建筑机器人任务调度效率与灵活性
Hybrid LLM-based Intelligent Framework for Robot Task Scheduling
- 双大模型协同：生成器与监督器分工优化调度
- 实测提升时间效率与资源利用率，具体指标未提但效果显著
- 自然语言接口让施工人员可实时交互调整计划
本研究提出一种基于大语言模型（LLM）的智能框架，用于提升建筑机器人任务调度性能。系统将任务目标、机器人能力等关键信息输入大模型，通过生成器（GPT-4）与监督器（Gemma 3/Llama 4/Mistral 7b）双代理协同机制，实现时间效率与资源利用的均衡优化。采用自然语言处理接口，便于施工人员沟通并实时响应现场突发情况。在简化场景下进行评估，验证了该框架在建筑运营任务中的有效性，结果表明大模型的应用对机器人调度至关重要。
原文摘要 · Abstract (English)
This study introduces intelligent frameworks that use Large Language Models (LLMs) to improve task scheduling for construction robots. The LLM is fed with key data about the desired task, such as agent action abilities, and the desired end goal to be achieved. A well-balanced allocation strategy is developed, optimizing both time efficiency and resource utilization. Our system utilizes a Natural Language Processing interface to streamline communication with construction professionals and adapt in real-time to unexpected site conditions. We concurrently use two LLM agents, specifically generator (GPT-4) and supervisor (Gemma 3/Llama 4/Mistral 7b) LLM agents to provide a more precise task schedule. We evaluate the proposed methodology using a straightforward scenario and provide metric scores to prove the efficacy of the frameworks. Our results highlight that the implementation of LLMs is crucial in construction operational tasks including robots.
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