SBIR/STTR Research Award

Automated Waste Sorting at the Point of Disposal

Award identifier 7a5d576c201ad3e104230b1227b2137706ea8b58d7cad465a41d1f63d4ae8f51Awarding agency Environmental Protection Agency

Award amount

$99,640

Program and phase

SBIR · Phase I

Award year

2022

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Award details

Awarding agency
Environmental Protection Agency
Branch
Not reported
Recipient company
CLEANROBOTICS
Proposal award date
2021-12-01
Contract end date
2022-05-31
UEI
PW44F2CLQWS8
Solicitation number
68HERC21R0144
Topic code
4B

Research abstract

CleanRobotics seeks to improve the US recycling system by using robotic sorting, powered by object detection, artificial intelligence and machine learning (AI/ML) to sort up between 2-4 streams (recycling, compost, landfill, etc) at the point of collection. This will improve the collection and sortation of recyclables in the US, where total collection rates are 35% and successful sortation rates are less than 50% (worse than random chance). CleanRobotics developed a robotic sorting system called TrashBot. TrashBot uses cloud storage and machine learning to determine which object users have dropped into the receptacle and the objects’ level of contamination.TrashBot then drops the item in the proper storage receptacle on the inside of the system. TrashBot identifies contaminated items and keeps them from entering the recycling stream. Each TrashBot unit is deployed with a screen. CleanRobotics uses these screens to provide educational content, especially as it relates to contamination. When users throw away contaminated recyclables, the system separates the contaminated item into the landfill and informs the user why the item was contaminated. In small scale trials, this method has proven effective at decreasing the amount of contaminates in the recycling stream. TrashBot collects data on every item users deposit. The system sends data to a centralized location where AI/ML systems determine patterns and identify trends in waste generation habits of TrashBot users. Cloud connectivity allows individual TrashBot units to learn from the global TrashBot fleet. This will increase the quality of the data we collect and drive AI/ML-based improvements to the TrashBot system over time. We analyze data collected in two ways: 1.) In the specific TrashBot deployment (i.e. the stadium, office building, airport, etc.) 2.) Across all TrashBot deployments. This data gives the CleanRobotics team and building managers insight into the kinds of items users are throwing away and trends in municipal solid waste generation. This improves AI/ML quality and allows us to tailor the messages we display on the TrashBot screen. CleanRobotics has conducted pilots and small-scale deployments of the TrashBot system. Customers include Google, The Port Authority of New York and New Jersey, AEG, Dallas Fort Worth International Airport, Pittsburgh International Airport, UNC Charlotte, and several other companies. In one year, TrashBots: 1.) Sorted 30,000 items with 90% accuracy , 2.) Diverted 1,800 lbs of recyclables compared to 650 lbs with conventional bins, 3.) Saved companies from emitting five tons of C02.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Research funding history

3 SBIR/STTR records share this UEI (2021–2023). Reported award amounts total $519,386. Reported amounts are not federal obligations.

Phases: Phase I, Phase II

Agencies: Environmental Protection Agency, Department of Defense

Agency intelligence

Environmental Protection Agency

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