AI 中文总结
该研究针对最后一公里配送中家具车内装箱的现实复杂条件，构建了基准数据集与PackingGPT框架，训练的LLP模型使装箱失败率降至0.67%，为该任务提供了可行方案。
AI 中文摘要
3D装箱是将矩形物品装入标准化容器，在几何运输自动化下最大化空间利用率的任务。将购买的家具装入私家车属于同一类任务，但条件更复杂，是标准容器装箱算法未考虑的情况。本文针对这些现实条件下的物理稳定放置问题，涉及异质箱体（如尺寸、重量不同）和已占用容器（如已装杂货）。本文为异质家具车内装箱任务提供了真实基准数据集和基线模型。该数据集采用真实家具公司的平板包装数据，通过按品类族外推，涵盖大量目录产品，包含多样的长、宽、高和重量。我们还提出了PackingGPT框架，将装箱视为受乐高组装过程启发的顺序放置，其中不同尺寸的异质箱体（积木）逐步放入不规则的剩余货舱空间（作品）。在我们的数据集上测试了五种基线装箱方法，均未考虑质心（CoM）约束。在轿车模拟中，平均有10%-40%的已装箱箱体未通过稳定性检查。当在放置过程中强制实施CoM约束的装箱序列上训练LLP模型后，失败率降至0.67%（SUV-500）。
英文摘要
3D bin packing rectangular items into standardised containers to maximise space utilisation under geometric shipping automation. Loading a furniture purchase into a personal vehicle is the same task, but under more complex conditions that standard container loading algorithms ignore. This paper addresses the physically stable placement under these realistic conditions with heterogeneous boxes (e.g. varying dimensions and weights) and occupied containers (e.g. groceries). This paper provides a real-world benchmark dataset and baseline model for the Heterogeneous furniture-in-vehicle packing task. The dataset uses real furniture company flat-pack packaging data covering a large number of catalogue products via family-level extrapolation with diversity length, widths, heights, and weights. We also propose a PackingGPT framework for packing as a sequential placement inspired by the Lego assembly process, where heterogeneous boxes of varying dimensions (bricks) are placed step-by-step into the irregular remaining cargo space (creations). Five baseline packing methods were tested on our dataset without considering the Centre-of- Mass (CoM) constraints. In sedan car simulations, 10-40% of placed boxes failed the stability check on average. When the LLP model was trained on packing sequences with CoM constraints enforced during placement, the failure rate dropped to 0.67% (SUV-500).
