AI & RoboticsNews

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.

According to the Environmental Protection Agency, the U.S. generated about 262 million tons of municipal waste in 2015 alone. While over 91 million tons were recycled and composted, automation promises to drive the total higher as the pandemic forces businesses to suspend recycling operations due to concerns for worker safety. That’s why the waste-sorting robotics market is set to reach $12.26 billion by 2024, increasing at a compound annual growth rate of 16.52%.

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).

AMP Cortex, the company’s robotics control system, leverages a combination of AI algorithms and physical robots to orchestrate sorting, picking, and placing tasks. A three-armed picker machine with an adaptable frame area and height sits over a conveyor belt, held in place by a moveable steel frame. It’s fed data from AMP Neuron, which uses computer vision to distinguish visual features and self-improves by processing “millions” of images in the cloud across AMP Robotics’ network. This allows it to more accurately sort objects and learn new classes of materials, adapt to packaging design and lighting changes; and recover high-demand materials like paper, tissue, and cardboard.

AMP Robotics

All this data feeds into AMP Insights, an online visualization tool that monitors material stream activity and performance. Real-time notifications sent via text and email keep managers abreast of goings-on, including potential equipment issues and hazards. And AMP Insights tracks key commodities to determine things like material composition per bale, known value created or lost on residual lines, and per-hauling load.

Earlier this year, AMP Robotics launched a lease program that offers interest-free financing, warranties, maintenance, and service packages without an upfront payment, with expenses totaling less than $6,000 a month in some cases.

AMP Robotics says that as of April, its platform had processed over a billion recyclables across installations in over 20 states, which the company claims translates to a reduction in greenhouse gas emissions of approximately half a million metric tons. Revenue was up 50% year-over-year, following the signing of a long-term agreement with Waste Connections to deploy 24 AI-guided robotics systems and the opening of AMP Robotics’ 40,000-square-foot test facility in Colorado. More recently, AMP Robotics extended its ability to identify recyclables to recover higher rates of materials like polypropylene resin, inking a deal with Keurig Dr Pepper in support of its introduction of recyclable K-Cup pods.

Valor Equity Partners and GV participated in AMP Robotics’ series B announced today, alongside existing investors Sequoia Capital, Sidewalk Infrastructure Partners, Congruent Ventures, and Closed Loop Partners. The round brings the company’s total raised to nearly $75 million.

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Author: Kyle Wiggers
Source: Venturebeat

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