SBIR Phase II: Advanced Artificial Intelligence for Robotic E-Commerce Pick-and-Pack Automation

小型企业创新研究(SBIR)第二阶段项目的更广泛影响/商业潜力是通过实施灵活的机器人材料处理系统来提高供应链的弹性。由人工智能控制的机器人系统。电子商务销售额每年增长20%。在新冠肺炎大流行期间，额外的零售量转移到了网上，许多客户习惯于使用电子商务采购必需品。这种转变给供应链基础设施带来了更大的负担，该基础设施传统上依赖人力来挑选、分类、包装和处理要交付的物品。这些人工流程单调、容易出错，有时还很危险，具有极高的员工流失率。这些流程的自动化提升了工作人员角色，并为流程带来了更大的一致性。在此第二阶段项目期间开发的创新可能会通过为专门为个人客户需求配置的人工智能机器人系统创建新的培训系统，从而实现复杂材料处理过程的更广泛自动化。这一创新可能会增强美国供应链的韧性，使公民无需出门就能快速可靠地获得食品、药品和健康用品等必需品。商业机会是巨大的，美国每年在挑选和打包工资上花费超过200亿美元。这个小企业创新研究(SBIR)第二阶段项目寻求开发新的方法，以快速训练支持人工智能(AI)的机器人系统，这些系统是为识别和操纵物体而构建的。仓库对象操作任务是可变的，自动化它们通常需要针对每个客户和设施的定制解决方案。这些定制解决方案往往昂贵得令人望而却步。为了解决这些问题，需要一种可以部署在多种物料处理流程配置中的工业操作系统。该项目旨在开发对扩大商业部署至关重要的模块，如质量控制视觉系统、物品可挑拣能力的自动评估，以及用于机器人挑选的增强型人工智能系统。该项目的预期结果是一个支持工业人工智能的机器人操作系统，允许快速配置机器人系统，以实施电子商务物流中挑选和包装单个物品的高度优化的流程。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to improve the resiliency of the supply chain by implementing flexible robotic systems for materials handling. The robotic systems that are controlled by artificial intelligence. E-commerce sales are increasing 20% year over year. During the COVID-19 pandemic additional retail volume shifted online and many customers became accustomed to sourcing essentials using e-commerce. This shift has put a greater burden on asupply chain infrastructure that has traditionally relied on human labor to pick, sort, pack, and process items for delivery. These manual processes are monotonous, error-prone, and sometimes dangerous, have extremely high worker turnover. The automation of these processes elevates worker roles and brings greater consistency to the processes. The innovation developed during this Phase II project may enable broader automation of complex materials handling processes by creating novel training systems for artificial intelligence-enabled robotic systems that are configured specifically for individual customer needs. This innovation may increase US supply chain resilience, enabling citizens to rapidly and reliably obtain necessities such as food, medicine, and health supplies without needing to leave their homes. The commercial opportunity is large, with over $20B spent on US pick and pack wages annually.This Small Business Innovation Research (SBIR) Phase II project seeks to develop new methods for rapidly training artificial intelligence (AI)-enabled robotic systems built for object identification and manipulation. Warehouse object manipulation tasks are variable and automating them often requires custom solutions for each customer and facility. These custom solutions are often prohibitively expensive. To solve these problems, an industrial operating system that can be deployed across many configurations of materials handling processes is required. This project aims to develop modules critical to scaling commercial deployments, such as quality control vision systems, automated assessments of item pickability, and enhanced AI systems for robotic picking. The anticipated result of this project is an industrial AI-enabled robotic operating system that allows rapid configuration of robotic systems to implement highly-optimized processes for picking and packing individual items in e-commerce logistics.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.