Shelf Replenishment Based on Object Arrangement Detection and Collapse Prediction for Bimanual Manipulation
Shelf Replenishment Based on Object Arrangement Detection and Collapse Prediction for Bimanual Manipulation
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DOI：
10.3390/robotics11050104
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发表时间：
2022-09
期刊：
影响因子：
3.7
通讯作者：
Tomohiro Motoda;Damien Petit;Takao Nishi;K. Nagata;Weiwei Wan;K. Harada
中科院分区：
文献类型：
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作者：
Tomohiro Motoda;Damien Petit;Takao Nishi;K. Nagata;Weiwei Wan;K. Harada
物流仓库中的对象操作自动化最近得到了积极的研究。然而，货架补货是一个挑战，需要精确和小心地处理密集堆积的物体。货架上物品的不规则排列使得这项任务特别困难。本文提出了一种从单个深度图像生成安全补给过程的方法，该方法作为两个网络的输入来识别排列模式并预测塌陷对象的发生。建议的基于推理的策略提供了一个适当的决定和行动过程中是否创建一个插入空间，同时考虑到货架内容的安全性。特别是，我们利用双手灵巧的操作能力的相关机器人安全地解决任务，而无需重新组织整个货架。实验与真实的双手机器人进行了三个典型的场景：搁置，堆叠，和随机。这些对象被随机放置在每个场景中。实验结果验证了我们所提出的方法在货架上的随机情况下与真实的双手机器人的性能。
Object manipulation automation in logistic warehouses has recently been actively researched. However, shelf replenishment is a challenge that requires the precise and careful handling of densely piled objects. The irregular arrangement of objects on a shelf makes this task particularly difficult. This paper presents an approach for generating a safe replenishment process from a single depth image, which is provided as an input to two networks to identify arrangement patterns and predict the occurrence of collapsing objects. The proposed inference-based strategy provides an appropriate decision and course of action on whether to create an insertion space while considering the safety of the shelf content. In particular, we exploit the bimanual dexterous manipulation capabilities of the associated robot to resolve the task safely, without re-organizing the entire shelf. Experiments with a real bimanual robot were performed in three typical scenarios: shelved, stacked, and random. The objects were randomly placed in each scenario. The experimental results verify the performance of our proposed method in randomized situations on a shelf with a real bimanual robot.