Machinery : Industrial Robot : Piece Picking
AMP’s pioneering AI platform, AMP Neuron™, applies computer vision to process millions of images to map complex material streams. Neuron applies deep learning to continuously improve the precise identification and categorization of paper, plastics, and metals by color, size, shape, opacity, form factor, brand, and more, contextualizing and storing data about each item it perceives. Unique in its depth and breadth, our global neural network enables automation to accurately recover recyclables, removing contamination and ultimately creating high-value raw material for resale into the global supply chain. Data captured also provides transparency to recyclers so they can optimize their operations, increase recycling rates, and economically process the feedstock needed so manufacturers of packaging, containers, and other goods can meet goals for recycled content and achieve society’s vision for a sustainable, circular economy.
Disrupting the Recycling Industry with AMP Robotics and Ansys
AMP Robotics Raises $91 Million in Series C Financing
AMP Robotics Corp. (“AMP”), a pioneer in artificial intelligence (AI), robotics, and infrastructure for the waste and recycling industry, has raised $91 million in corporate equity in a Series C financing, led by Congruent Ventures and Wellington Management as well as new and existing investors including Blue Earth Capital, Sidewalk Infrastructure Partners (SIP), Tao Capital Partners, XN, Sequoia Capital, GV, Range Ventures, and Valor Equity Partners. This new round of funding follows a $55 million Series B financing led by XN in January 2021.
AMP will use the latest funding to scale its business operations while continuing its international expansion. Demand for robotics to retrofit existing recycling infrastructure continues to thrive; among historic demand for recycled commodities of all types, the industry needs capacity to meet the 2025 goals of consumer packaged goods companies that have committed to the use of post-consumer recycled (PCR) content. The company’s core technology business has grown accordingly; the new capital will enhance manufacturing capacity to support a fleet of approximately 275 robots around the world and further AMP’s ongoing development of AI-enabled automation applications for recycling, like AMP Vortex™, the company’s latest innovation for recovery of film and flexible packaging. AMP also has three production facilities in the Denver, Atlanta, and Cleveland metropolitan areas; the funding will help drive further growth of the company’s secondary sortation business in the United States.
AMP Robotics Develops Industry’s First AI-Powered System for Recovery of Film and Flexible Packaging
AMP Robotics Corp. (“AMP”), a pioneer in artificial intelligence (AI), robotics, and infrastructure for the waste and recycling industry, is developing an AI-powered automation solution to improve recovery of film and flexible packaging. This first-of-its-kind innovation for materials recovery facilities (MRFs) aims to tackle the persistent challenge of film contamination.
A mere 1 percent of U.S. households have curbside access for recycling film and flexible packaging today, estimates The Recycling Partnership. Yet film and flexibles comprise the fast-growing and second-largest valued packaging segment, behind only corrugated containers and ahead of bottles and other rigid plastic packaging. Close to 95 pounds of these materials, including grocery and storage bags, pouches, and wrappers, are found in the average U.S. home each year. AMP’s solution, AMP Vortex™, is the industry’s first AI-powered automation system for film removal and recovery in MRF environments. AMP’s system targets film contamination and is initially optimized for quality control on fiber lines. Vortex provides the industry with the most flexible and adaptable solution targeting film; it can be deployed as a retrofit solution in various configurations to accommodate different belt sizes and inclines.
AI-Guided Robots Are Ready to Sort Your Recyclables
So how much of the material that goes into the typical bin avoids a trip to landfill? For countries that do curbside recycling, the number—called the recovery rate—appears to average around 70 to 90 percent, though widespread data isn’t available. That doesn’t seem bad. But in some municipalities, it can go as low as 40 percent.
Getting AI into the recycling business means combining pick-and-place robots with accurate real-time object detection. Pick-and-place robots combined with computer vision systems are used in manufacturing to grab particular objects, but they generally are just looking repeatedly for a single item, or for a few items of known shapes and under controlled lighting conditions. Recycling, though, involves infinite variability in the kinds, shapes, and orientations of the objects traveling down the conveyor belt, requiring nearly instantaneous identification along with the quick dispatch of a new trajectory to the robot arm.
One Michigan county makes millions by recycling. It could become a state model.
Today, Emmet County’s high-tech recycling program has grown into a million-dollar revenue source for the community of 33,000-some residents, selling thousands of tons of recyclables to companies across Michigan and the Great Lakes region to be made into new products. They even found a way to recycle plastic shopping bags.
Inside the facility in Harbor Springs, a robotic arm quickly sweeps across a moving conveyer belt and plucks high-grade plastics, glass, and aluminum, dropping them into sorted bins. The stream of mixed containers flows around and around until the robot pulls out all the recyclable items at a rate of 90 picks per minute; another line of materials in a separate room is where workers pluck papers, boxes, and bags by hand from a moving conveyor belt.
Revolutionizing the Composting Industry
“To our knowledge, this facility is the first time that AI (artificial intelligence) and robotics have been used in a pre-sort facility for organics in North America,” says McMillin. “The goal of the presort facility is to remove contaminants from the organic waste stream prior to processing instead of trying to remove those contaminants after they’ve been through the composting process via vacuums and wind sifters that have historically been attached to the screening process.
Trash to Cash: Recyclers Tap Startup with World’s Largest Recycling Network to Freshen Up Business Prospects
People worldwide produce 2 billion tons of waste a year, with 37 percent going to landfill, according to the World Bank.
“Sorting by hand on conveyor belts is dirty and dangerous, and the whole place smells like rotting food. People in the recycling industry told me that robots were absolutely needed,” said Horowitz, the company’s CEO.
His startup, AMP Robotics, can double sorting output and increase purity for bales of materials. It can also sort municipal waste, electronic waste, and construction and demolition materials.
AMP Robotics raises $55 million for AI that picks and sorts recyclables
AMP Robotics, a Denver, Colorado-based startup creating robotic systems that sort recyclable material, this morning announced it has closed a $55 million series B funding round led by XN. The startup says it will use the funds to scale its business operations and develop AI product applications that integrate into materials recovery facilities to increase recycling rates for its customers.
AMP Robotics claims its platform delivers higher pick rates (80 items per minute) than manual processes, as well as holistic monitoring of material streams without retrofitting. It is modular in design, enabling facilities managers to adapt it to existing workflows, and it’s tailored to individual brands and SKUs of recyclable objects. AMP Robotics’ products can sort not only metals, batteries, capacitors, plastics, PCBs, wires, cartons, bottlecaps, cardboard, cups, clamshells, lids, aluminum, and thin film by color, clarity, and opacity, but also materials made of metal, mixed wood, asphalt, bricks, concrete, and mixed plastics (e.g., .polyethylene terephthalate, high-density polyethylene, low-density polyethylene, polypropylene, and polystyrene).