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

Rstream Recycling Inc, Somerville MA

Investigators

Abstract

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II 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 non-industrial settings (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 NSFs 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 waste sortation and classification in cluttered environments. This is enabled by waste organization technology developed during NSF SBIR Phase I research allowing for increasingly complex (or diverse) structures (shapes, sizes, and materials) to be accurately identified and subsequently sorted. In under 150 square feet, 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) Building a compact, high-throughput, high-accuracy, and turnkey sorting system for HTVs; 2) Developing computer vision (CV) technology to enhance waste classification model accuracy, detail, and robustness with machine learning (ML); and 3) Conducting an extended pilot study on the developed system with an HTV partner. 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.