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SBIR Phase I: A Handheld Fine-Grained Radio Frequency IDentification (RFID) Localization System for Retail Automation

SBIR Phase I: A Handheld Fine-Grained Radio Frequency IDentification (RFID) Localization System for Retail Automation
SBIR 第一阶段:用于零售自动化的手持式细粒度射频识别 (RFID) 定位系统
批准号:
2232748
负责人:
Isaac Perper
金额:
$27.5万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2023
资助国家:
美国
项目状态:
已结题
起止时间:
2023-04-01 至 2024-05-31

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中文摘要
翻译
这一小企业创新研究(SBIR)第一阶段项目的更广泛/商业影响将保护零售商店免受商品损失。 实体零售业正在经历前所未有的转型,在过去十年中,由于劳动力短缺、电子商务巨头的竞争以及现代消费者不断变化的期望,实体零售业损失了数十亿美元。为了解决这些问题,零售商一直在采用新的数字技术来了解他们的库存,优化商店运营,并获得客户洞察。超过90%的美国零售商采用的一项关键技术是射频识别(RFID)。RFID标签是一种便宜、无线和无电池的贴纸(类似于条形码),使零售商能够实现准确的全店库存,从而为零售商带来显著的收入增长。与现有的(便携式)RFID技术相比,现有的RFID技术只能确定RFID标记的物品是否在商店中(即,库存),所提出的技术旨在精确地定位这些物品在整个商店。该技术利用了数十亿个现成的超高频(UHF)RFID标签,这些标签已经附着在服装,鞋类和服装上。与只能检测RFID标签物品的现有移动的解决方案相比,该团队的手持设备利用复杂的信号激励和处理技术,以分米级的精度确定每个RFID的确切位置。SBIR第一阶段项目将建立一个能够识别和精确定位RFID标签物品的系统,包括三个主要创新组件:(1)用于定位RFID的便携式手持无线设备,(2)可扩展的云和边缘计算平台,以处理和存储数据,以及(3)移动的和Web用户界面,用于访问数据并优化零售店员工的拣选任务。实现端到端平台需要开发高效的传感器融合算法和低功耗、低成本的硬件,以实现准确、鲁棒和低延迟的定位。该技术需要解决边缘设备上的计算、内存、带宽和功率限制所带来的挑战。该平台还需要开发分离和云计算架构,以实时有效地处理来自多个手持设备的数据,并提供可通用的应用程序编程接口(API),以将此数据管道与零售客户集成。到第一阶段结束时,该项目将在一家零售店中试用完全集成的系统,以评估其实际性能。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
The broader/commercial impacts of this Small Business Innovation Research (SBIR) Phase I project will protect retail stores from loss of merchandise. Brick-and-mortar retail is undergoing an unprecedented transformation, having lost billions of dollars over the past decade due to labor shortages, competition from e-commerce giants, and changing expectations from the modern consumer. To address these issues, retailers have been adopting new digital technologies to gain visibility into their inventory, optimize store operations, and gain customer insights. A key technology that has been adopted by over 90% of US retailers, is Radio Frequency IDentification (RFID). RFID tags are cheap, wireless, and battery-less stickers (similar to barcodes) that have allowed retailers to achieve accurate store-wide inventory, resulting in a significant revenue increase for retailers. In contrast to existing (portable) RFID technology which can only determine whether RFID-tagged items arein the store (i.e., inventory), the proposed technology aims to precisely locate these items throughout the store. The technology leverages billions of off-the-shelf ultra-high frequency (UHF) RFID tags that are already attached to clothing, footwear, and apparel items. In contrast to existing mobile solutions which can only detect RFID-tagged items, the team's handheld device leverages sophisticated signal excitation and processing techniques to pin down each RFID’s exact position with decimeter-scale accuracy. This SBIR Phase 1 project will build a system capable of identifying and precisely locating RFID-tagged items and includes three main innovative components: (1) a portable, handheld wireless device for locating RFIDs, (2) a scalable cloud and edge computing platform to process and store the data, and (3) a mobile and web user interface for accessing the data and optimizing picking tasks for retail store associates. Realizing the end-to-end platform requires developing efficient sensor fusion algorithms and low-power, low-cost hardware for accurate, robust, and low-latency localization. This technology necessitates addressing challenges that arise from the computational, memory, bandwidth, and power constraints on the edge device. The platform also requires developing the split and cloud computing architecture to efficiently process data from multiple handheld devices in real-time as well as provide the generalizable application programming interfaces (APIs) to integrate this data pipeline with the retail customers. By the end of the Phase I period, the project will have piloted the fully-integrated system in a retail store to evaluate its real-world performance.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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