SBIR-STTR Award

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.

CleanRobotics has developed a robotic, artificial intelligence (AI) powered system, called TrashBot, for sorting and auditing recycling, landfill, and compost from facilities with transient populations. Our system has the unique capability of ensuring proper waste sorting of up to four waste streams with the added cost reduction of eliminating user intervention. Discarding waste becomes an educational experience as the TrashBot monitor provides relevant information to the user. Each unit can be customized for specific needs through custom AI algorithms based on use type, local recycling requirements, etc. and customized messaging can be programmed into each unit for the end-user. This addresses the challenge of users properly disposing of waste and avoids contamination of waste streams through introduction of improper materials, such as food waste. Lacking proper education about their own recycling efforts, consumers unintentionally contaminate existing recycling streams by discarding recyclables and recyclable containers that contain food waste, especially in public areas where food is purchased and packaged in single-use plastics, such as airports, hospitals, and event venues. Clean Robotics’ technology removes the sorting responsibilities from the consumer at the discard point, using AI, ML, and computer vision to sort, analyze and track the amount and type of recyclables that are being discarded. Removing user intervention improves the quality of the recycling stream with no additional effort from the consumer. This point also provides a captive moment in which consumers can be educated about how to recycle better, based on behaviors identified through that TrashBot’s analytics. Because this information is presented in a novel manner, it is retained by the consumer, improving recycling behaviors at discard points where TrashBots are not in use. Our Phase 1 project, “Automated Waste Sorting at the Point of Disposal,” successfully improved recycling efficiency and reduced carbon emissions on a hospital campus by using AI, computer vision, and robotics to sort waste. TrashBot technology allowed for 300% more accurate sorting than relying on the hospital population to manually sort with conventional trash receptacles. According to Global Market Study, the Global Internet of Bins market is projected to be $25.7B by 2029. A Babson College study estimated the United States waste bin market is $12B Total Addressable Market.