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

这项小型企业创新研究（SBIR）第一期计划的更广泛影响/商业潜力，是改善堆填区的分流及非工业废物的循环再造率。该项目针对高流量场馆（htv），如体育场馆、大学、机场和主题公园。废物流分类以区分回收和填埋的材料类别，目前采用的要么是基本的、劳动密集型的人工分类，要么是昂贵的、技术复杂的机器人分类，对于中小型设施（即hvs）来说，这两种方法都不是可行的选择，因为这些设施由于污染而承担了材料回收设施所带来的更高费用。这些费用阻碍了回收，导致了日益严重的废物问题。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），并与美国国家科学基金会促进国家健康、繁荣和福利的使命保持一致。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模拟（即，在1 × 1流中排列对象）和在混乱环境中分类，允许越来越复杂（或多样化）的结构（形状，大小和材料）被准确识别并随后分类。这种方法产生了一个有序的对象流，然后可以根据任何分流方案进行分类，以实现有效的回收。该软件使用半监督学习，允许从集中训练集进行领域适应，从而能够快速实施特定地点废物流的优化分类方案，所需的人工干预比传统方法少得多。成功的开发将导致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，更少的资源密集，从而提供一种新颖的交钥匙解决方案，可以在现场采用。研究目标包括：1)开发和评估利用机电过程进行废物流模拟的硬件组件；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增强方法；3)验证所开发的系统在类噪声条件下对可回收物的高精度模拟和分类。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 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.