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.

这项小型Busisness创新研究（SBIR）I期项目的更广泛的影响/商业潜力在于改善垃圾填埋场的转移和非工业废物的回收率。该项目针对高流量场所（HTV），其受控废物流，例如体育场，大学，机场和主题公园。目前，使用基本的，劳动密集型的手动排序或昂贵且技术复杂的机器人排序进行回收和垃圾填埋的材料类别的废物流分类，这两种方法都是可行的小型设施（即HTV）的可行选择（即HTVS），然后通过材料恢复设施施加了高度的高度率。这些费用阻止了回收利用，导致了越来越多的废物问题。该项目旨在开发一种智能的废物分类系统，该系统利用计算机愿景和创新的硬件来实现现场，分散的分类，并支持重新获得6600万吨和2000亿美元的可回收材料，这些材料目前每年都浪费。通过减轻垃圾填埋场的废物积累以及与维珍资源挖掘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的使命相吻合，该使命是推进国家健康，繁荣和福利。所提出的技术由一个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单击（即，在1 x-1流中的布置对象）和在混乱环境中进行分类，从而使越来越复杂的（或潜水员）结构（形状，大小和材料）准确地识别并进行分类。这种方法会产生有序的对象流，然后可以根据任何有效回收的转移方案对其进行分类。该软件使用半监督的学习来允许从集中式培训集中适应领域的适应性，从而可以快速实施特定地点废物流的优化排序方案，而人类的干预措施比传统上所需的需要少得多。成功的开发将导致一个简化的排序平台，比现有的废物排序操作更便宜，更强大且资源更少，从而提供了一种新颖的交钥匙解决方案，可以在现场采用。研究对象包括：1）使用机电过程来开发和评估硬件组件，以进行废水流动； 2）开发用于高效数据集生成和废物流分类的软件，尤其是使用半监督的学习和数据增强方法； 3）验证开发的系统在噪音之类的噪音下对可回收物的高精度单格和分类。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，被认为是珍贵的支持。