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

这个小企业创新研究（SBIR）第二阶段项目的更广泛的影响/商业潜力是通过实施灵活的机器人系统进行物料处理来提高供应链的弹性。由人工智能控制的机器人系统。电子商务销售额同比增长20%。在2019冠状病毒病大流行期间，额外的零售量转移到网上，许多客户习惯于通过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，传统上，供应链基础设施依赖于人力来挑选、分类、包装和处理交付的物品。这些手工流程单调、容易出错，有时还很危险，员工流动率极高。这些过程的自动化提升了工作者角色，并为过程带来了更大的一致性。在这个第二阶段项目中开发的创新可以通过为人工智能机器人系统创建新的培训系统来实现复杂材料处理过程的更广泛自动化，这些系统是专门为个人客户需求配置的。这一创新可能会提高美国供应链的弹性，使公民无需离开家就能快速、可靠地获得食品、药品和卫生用品等必需品。商业机会是巨大的，美国每年花在挑选和包装工资上的钱超过200亿美元。这项小企业创新研究（SBIR）第二阶段项目旨在开发新的方法，用于快速训练用于对象识别和操作的人工智能（AI）机器人系统。仓库对象操作任务是可变的，自动化它们通常需要为每个客户和设施定制解决方案。这些定制的解决方案通常非常昂贵。为了解决这些问题，需要一个可以跨多种材料处理过程配置部署的工业操作系统。该项目旨在开发对扩展商业部署至关重要的模块，如质量控制视觉系统、物品可拾取性的自动评估，以及用于机器人拾取的增强人工智能系统。该项目的预期结果是一个工业人工智能机器人操作系统，允许机器人系统的快速配置，以实现高度优化的流程，在电子商务物流中挑选和包装单个物品。该奖项反映了美国国家科学基金会的法定使命，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。

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.