发表机构
Orange Innovation; CNRS（Orange创新公司; 法国国家科学研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CoAdapt利用大语言模型作为运行时融合控制器，根据空间配置和网络状态动态选择参与机器人及融合算法，在OPV2V基准上实现通信成本降低38%且检测精度不变。
AI 中文摘要
工业物联网环境越来越多地部署自主移动机器人，用于物料搬运、产品组装或基础设施检查等任务。在此类部署中，协同感知使机器人能够共享激光雷达观测数据，并共同构建比单个智能体单独所能生成的更丰富的环境模型。然而，工业环境是动态空间，机器人位置不断变化，网络带宽波动，机器人对感知质量的边际贡献在运行时也会发生变化。现有的协同感知方法是为静态参与假设而设计的，无法在不牺牲检测精度或通信效率的情况下适应这些动态变化。本文提出了CoAdapt，一种面向工业物联网机器人集群的自适应协同感知框架，其中大语言模型（LLM）作为运行时融合控制器，根据当前空间配置和网络状态，共同决定哪些机器人参与融合过程以及应用哪种融合算法。该大语言模型基于从原始激光雷达点云派生的场景的结构化自然语言描述进行推理，无需特定任务训练，并能泛化到未见过的集群拓扑。在OPV2V基准上跨25个场景的评估中，我们的方法在保持与静态基线方法相当的检测精度的同时，实现了通信成本降低38%。
英文摘要
Industrial IoT environments increasingly deploy autonomous mobile robots for tasks such as material handling, product assembly, or infrastructure inspection. In such deployments, collaborative perception enables robots to share LiDAR observations and collectively construct a richer model of their environment than an individual agent could produce alone. However, industrial environments are dynamic spaces where robot positions shift continuously, network bandwidth fluctuates, and the marginal contribution of robots to perception quality varies at runtime. Existing collaborative perception approaches are designed for static participation assumptions and cannot adapt to these dynamics without sacrificing either detection precision or communication efficiency. This paper presents CoAdapt, an adaptive collaborative perception framework for IIoT robotic swarms in which a Large Language Model (LLM) serves as a runtime fusion controller, jointly deciding which robots participate in the fusion process and which fusion algorithm to apply based on the current spatial configuration and network state. The LLM reasons over structured natural language descriptions of the scene derived from raw LiDAR point clouds, requiring no taskspecific training and generalizing to unseen swarm topologies. Evaluated on the OPV2V benchmark across 25 scenarios, our approach achieves a 38% reduction in communication cost while maintaining detection precision comparable to static baseline approaches.
Journal refThe 7th IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS 2026), Sep 2026, Cesena, Italy
