Robot-executed longitudinal sloshing dataset for rectangular tank liquid handling

This dataset supports the manuscript Vision-Based Measurement and Large-Scale Experimental Validation of an Analytical Model for Longitudinal Sloshing in Rectangular Tanks for Robotic Liquid Handling. The dataset contains robot-executed longitudinal transport trajectories and the corresponding experimentally measured liquid response used for the large-scale validation of an equivalent mass-spring-damper (MSD) model for longitudinal sloshing in a rectangular tank. The experimental setup consists of a T75 cell culture flask moved by an industrial manipulator. The liquid response was measured with a compact, non-intrusive, single-camera side-view vision-based measurement system. The measured output is the longitudinal wall-wave elevation, consistently with the output variable adopted in the analytical MSD model. The dataset includes 9822 valid trajectories. The first 5000 trajectories are standard trajectories generated using motion laws commonly adopted in robotics and industrial automation, including polynomial splines, trapezoidal and modified trapezoidal profiles, harmonic motion and cycloidal motion. The remaining 4822 trajectories are optimized trajectories generated by exploiting the equivalent MSD model to reduce sloshing while preserving a smooth acceleration profile. The main data files are: INPUT_TRAIN.npy: imposed longitudinal acceleration trajectories of the container, in m/s2; OUTPUT_TRAIN.npy: vision-based measured longitudinal wall-wave elevation, in mm; trajectory_metadata.csv: per-trajectory labels, subset information and summary quantities; time_vector.csv: reconstructed sample index and time vector; load_dataset.py: minimal Python script to load, inspect and plot the dataset. The arrays have shape (9822, 1500). Each row corresponds to one robot-executed trajectory, and the same row index links the input acceleration and the measured wall-wave response. The validation interval used in the manuscript corresponds to the first 750 samples, i.e. 3 s, including both the active motion phase and the initial free-decay response. The dataset is intended to support reproducibility and future benchmarking of sloshing measurement and prediction methods under realistic robotic operating conditions.本数据集用于支撑题为《面向机器人液体处理的矩形储罐纵向晃荡分析模型的视觉测量与大规模实验验证》的学术稿件。 本数据集包含机器人执行的纵向运输轨迹，以及对应的实验实测液体响应数据，用于对矩形储罐纵向晃荡的等效质量-弹簧-阻尼（mass-spring-damper, MSD）模型开展大规模验证。 实验装置采用工业机械臂驱动T75细胞培养瓶，通过紧凑型非侵入式单相机侧视视觉测量系统采集液体响应，测得的输出为纵向壁面波高，与分析型MSD模型采用的输出变量保持一致。 本数据集共包含9822条有效轨迹。其中前5000条为采用机器人与工业自动化领域常用运动规律生成的标准轨迹，涵盖多项式样条、梯形与修正梯形曲线、简谐运动以及摆线运动。剩余4822条为利用等效MSD模型生成的优化轨迹，可在抑制液体晃荡的同时保持平滑的加速度分布。 核心数据文件如下： INPUT_TRAIN.npy：容器施加的纵向加速度轨迹，单位为m/s²； OUTPUT_TRAIN.npy：基于视觉测量的纵向壁面波高，单位为mm； trajectory_metadata.csv：单轨迹标签、子集信息与汇总统计量； time_vector.csv：重构采样索引与时间向量； load_dataset.py：用于加载、检视与绘制数据集的极简Python脚本。 上述数组的形状均为(9822, 1500)，每一行对应一条机器人执行的轨迹，相同行索引可关联输入加速度与测得的壁面波响应。论文中采用的验证区间为前750个采样点（即3秒时长），涵盖主动运动阶段与初始自由衰减响应阶段。 本数据集旨在为实际机器人作业场景下的晃荡测量与预测方法的可复现性研究及未来基准测试提供支撑。