导电型基于聚合物的交互式棚架系统,用于实时库存管理
Musafargani Sikkandhar1, Ruiqi Lim1, Ramona B Damalerio1
1Institute of Microelectronics, A*STAR (Agency for Science, Technology and Research), Singapore 138634, Singapore.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
一个新的智能货架系统使用导电聚合物传感器来根据形状和重量识别商品,减少零售库存. 这种实时库存管理系统达到99.2%的准确性,为自动化商店铺平了道路.
科学领域:
- 材料科学:开发一种基于导电聚合物的新型传感器阵列,用于交互式货架.
- 计算机科学:深度学习模型的应用,用于实时库存管理和库存单位的识别.
背景情况:
- 零售店库存不足是一个重大问题,由于手工错误和物品错位,导致高达4%的销售损失.
- 目前的实时库存管理系统用于检测零售环境中错位或缺失的库存是有限的.
- 解决这些局限性需要创新的解决方案,以准确和自动化的库存跟踪.
研究的目的:
- 开发和评估一个导电的基于聚合物的交互式货架系统,用于实时零售库存管理.
- 为了能够准确地检测和识别库存单位 (SKU),使用它们的形状和重量特征.
- 通过先进的库存跟踪来探索创建自动化",无人"零售环境的潜力.
主要方法:
- 制造一个80 × 48的传感器阵列使用幕打印的基于碳的导电聚合物在柔性基板上与金色互数字的电极.
- 使用每一个5mm × 5mm感应像素来检测单个SKU的形状和重量特征.
- 使用TensorFlow深度学习模型基于传感器数据进行SKU识别,包括处理堆叠的项目.
主要成果:
- 开发的传感器系统证明了可靠的功能阻力范围,均性,可重复性和耐用性.
- 仅仅使用形状特征,SKU识别准确度达到95%,并将形状和重量特征结合起来,提高到99.2%.
- 该系统有效地检测到堆叠的SKU,并显示了在零售环境中跨多个货架集成的模块化.
结论:
- 一个新的深度学习嵌入式交互式智能货架系统已成功开发用于零售库存管理.
- 该系统利用SKU的形状和重量特征来实现高度准确的实时库存跟踪和库存缺口检测.
- 拟议的技术有助于开发自动化零售环境,并显著提高库存管理效率.
相关概念视频
Energy Stored in a Capacitor
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