SBIR Phase I: Autonomous waste sorting platform for decentralized pre-processing

SBIR 第一阶段：用于分散预处理的自主垃圾分类平台

• 批准号: 2223186
• 负责人: Ethan Walko
• 金额: $ 27.5万
• 依托单位: RSTREAM RECYCLING LLC
• 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
• 起止时间: 2023-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2223186&HistoricalAwards=false
• 关键词: SBIR; Phase; Autonomous; waste; sorting

The broader impact/commercial potential of this Small Busisness Innovation Research (SBIR) Phase I project is in improving landfill diversion and recycling rates of non-industrial waste. This project targets high-traffic venues (HTVs) with controlled waste streams such as stadiums, universities, airports, and theme parks. Waste stream sorting to differentiate classes of materials for recycling and landfill is currently undertaken either using rudimentary, labor-intensive manual sorting, or expensive and technically complex robotic sorting, neither of which are feasible options for small- to mid-sized facilities (i.e., HTVs), which then shoulder heightened rates imposed by material recovery facilities due to contamination. These expenses have discouraged recycling, contributing to the mounting waste problem. This project seeks to develop an intelligent waste-sorting system that leverages computer vision and innovative hardware to enable on-site, decentralized sorting, facilitating the recapture of the 66 million tons and $200 billion worth of recyclable materials that currently go to waste each year. By mitigating waste accumulation in landfills and the greenhouse gas emissions associated with virgin resource mining, this technology supports United Nations sustainable development goals 11 (sustainable cities and communities) and 12 (responsible consumption and production) and aligns with the NSF’s mission of advancing national health, prosperity, and welfare. The proposed technology consists of a hardware-software solution that uses the latest in computer vision to perform automated singulation (i.e., arranging objects in a 1-by-1 stream) and classification in cluttered environments, allowing for increasingly complex (or diverse) structures (shapes, sizes, and materials) to be accurately identified and subsequently sorted. This approach produces an ordered stream of objects, which can then be sorted according to any diversion scheme for efficient recycling. The software uses semi-supervised learning to allow for domain adaptation from a centralized training set, enabling rapid implementation of optimized sorting schemes of site-specific waste streams, requiring significantly less human intervention than traditionally needed. Successful development would result in a simplified sorting platform that is cheaper, more robust, and less resource intensive than existing waste sorting operations, thus offering a novel turnkey solution that could be feasibly adopted on-site. Research objectives include: 1) Developing and evaluating hardware assemblies using electromechanical processes for waste stream singulation; 2) Developing software for efficient dataset generation and waste stream classification, particularly using semi-supervised learning and data augmentation approaches; and 3) Validating the developed system for high precision singulation and classification of recyclables under noisy like conditions.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.

这个小型企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是提高非工业废物的填埋转移和回收率。该项目的目标是具有受控废物流的高流量场所（HTV），如体育场，大学，机场和主题公园。废物流分类以区分用于回收和填埋的材料的类别，目前要么使用基本的劳动密集型人工分类，要么使用昂贵且技术复杂的机器人分类，这两种方法都不是中小型设施的可行选择（即，HTV），然后承担材料回收设施因污染而征收的更高费率。这些费用阻碍了回收，导致废物问题日益严重。该项目旨在开发一种智能垃圾分类系统，利用计算机视觉和创新硬件实现现场分散式分类，促进回收目前每年浪费的6600万吨和价值2000亿美元的可回收材料。通过减少垃圾填埋场的废物积累和与原始资源开采相关的温室气体排放，该技术支持联合国可持续发展目标11（可持续城市和社区）和12（负责任的消费和生产），并与NSF促进国民健康，繁荣和福利的使命保持一致。所提出的技术由硬件-软件解决方案组成，该解决方案使用最新的计算机视觉来执行自动分割（即，将对象排列在1对1的流中）并在杂乱的环境中进行分类，从而允许准确地识别并随后分类日益复杂（或多样化）的结构（形状、大小和材料）。这种方法产生了一个有序的对象流，然后可以根据任何转移方案进行分类，以实现有效的回收。该软件使用半监督学习，允许从集中式训练集进行域适应，从而能够快速实施特定场地废物流的优化分类方案，所需的人工干预比传统所需的要少得多。成功的开发将产生一个简化的分类平台，比现有的废物分类操作更便宜，更强大，资源密集度更低，从而提供一个新的交钥匙解决方案，可以在现场可行地采用。 研究目标包括：（1）开发和评估使用机电工艺进行废物流分离的硬件组件;（2）开发用于高效数据集生成和废物流分类的软件，特别是使用半监督学习和数据扩充方法;和3）验证开发的系统，用于在类似噪音的条件下对可回收物进行高精度分离和分类。该奖项反映了NSF的法定使命，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，认为值得支持。