发表机构
KTH Royal Institute of Technology; Digital Futures, KTH Royal Institute of Technology（瑞典皇家理工学院; 数字未来，瑞典皇家理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该论文介绍了一个自主移动机器人运营物流数据集MoRoOp，包含作业、调度事件和机器人状态，用于支持AI智能体评估、扰动检测和仿真研究。
AI 中文摘要
自主移动机器人（AMR）日益在生产物流中执行物料运输，其运行由作业生成、调度和机器人控制所支配。我们提出MoRoOp，一个在实验室试剂制备和供应场景中记录的AMR运营数据集，涵盖九个八小时班次。在每个班次中，AMR执行随机生成的试剂供应、空箱补充和充电作业。该数据集关联了作业规格、构成每个作业的操作、记录操作状态转换和结果的调度事件，以及包含位置、方向、速度、每个车轮的充电状态和诊断信息的机器人状态观测。它包含1,382个作业、4,815个操作、19,352个调度事件和140,386个机器人状态观测，以及作业生成中使用的试剂规格。数据集在运行中的实验室环境中记录，并保留了延迟、导航受阻和未成功操作等技术上有效的观测。提供了原始和清理后的机器人状态表。记录的复用方向包括在运营决策记录上评估AI智能体、扰动检测、操作预测、数据驱动仿真和事件日志分析。
英文摘要
Autonomous mobile robots (AMRs) increasingly perform material transport in production logistics, where their operation is governed by job generation, dispatching and robot control. We present MoRoOp, a dataset of AMR operations recorded in a laboratory kit preparation and supply scenario over nine eight-hour shifts. During each shift, an AMR executed stochastically generated kit supply, empty-box refill and charging jobs. The dataset links job specifications, the operations constituting each job, dispatch events documenting operation state transitions and outcomes, and robot-state observations comprising position, orientation, velocity, per-wheel state of charge and diagnostics. It contains 1,382 jobs, 4,815 operations, 19,352 dispatch events and 140,386 robot-state observations together with the kit specifications used during job generation. The dataset was recorded in an operating laboratory environment, and technically valid observations of delays, obstructed navigation and unsuccessful operations were retained. Both raw and cleaned robot-state tables are provided. Documented reuse directions include the evaluation of AI agents on operational decision records, disturbance detection, operation prediction, data-driven simulation and event-log analysis.
CommentsSubmitted to Nature Scientific Data
