The integration of large language models (LLMs) with robotics has significantly advanced robots' abilities in perception, cognition, and task planning. The use of natural language interfaces offers a unified approach for expressing the capability differences of heterogeneous robots, facilitating communication between them, and enabling seamless task allocation and collaboration. Currently, the utilization of LLMs to achieve decentralized multi-heterogeneous robot collaborative tasks remains an under-explored area of research. In this paper, we introduce a novel framework that utilizes LLMs to achieve decentralized collaboration among multiple heterogeneous robots. Our framework supports three robot categories, mobile robots, manipulation robots, and mobile manipulation robots, working together to complete tasks such as exploration, transportation, and organization. We developed a rich set of textual feedback mechanisms and chain-of-thought (CoT) prompts to enhance task planning efficiency and overall system performance. The mobile manipulation robot can adjust its base position flexibly, ensuring optimal conditions for grasping tasks. The manipulation robot can comprehend task requirements, seek assistance when necessary, and handle objects appropriately. Meanwhile, the mobile robot can explore the environment extensively, map object locations, and communicate this information to the mobile manipulation robot, thus improving task execution efficiency. We evaluated the framework using PyBullet, creating scenarios with three different room layouts and three distinct operational tasks. We tested various LLM models and conducted ablation studies to assess the contributions of different modules. The experimental results confirm the effectiveness and necessity of our proposed framework.

翻译：大语言模型（LLM）与机器人技术的融合显著提升了机器人在感知、认知与任务规划方面的能力。自然语言接口为表达异构机器人的能力差异、促进机器人间通信以及实现无缝任务分配与协作提供了统一途径。目前，利用LLM实现去中心化的多异构机器人协作任务仍是一个研究不足的领域。本文提出了一种新颖的框架，利用LLM实现多异构机器人间的去中心化协作。我们的框架支持移动机器人、操作机器人与移动操作机器人三类机器人协同工作，以完成探索、运输与整理等任务。我们开发了一套丰富的文本反馈机制与思维链（CoT）提示，以提升任务规划效率与整体系统性能。移动操作机器人可灵活调整其基座位置，确保抓取任务处于最佳条件。操作机器人能够理解任务需求，在必要时寻求协助，并妥善处理物体。与此同时，移动机器人可广泛探索环境、映射物体位置，并将此信息传递给移动操作机器人，从而提高任务执行效率。我们使用PyBullet对该框架进行了评估，构建了三种不同房间布局与三种不同操作任务的场景。我们测试了多种LLM模型，并进行了消融实验以评估不同模块的贡献。实验结果证实了我们所提出框架的有效性与必要性。