FEATURE
SPOTLIGHT SARAH WILLIS: “Automating waste sorting brings the benefit of transparency and traceability of the materials sorted” Interview | rob coker
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DURING THE FANUC OPEN HOUSE EVENT, ROB COKER SPOKE WITH SARAH WILLIS, HEAD OF MARKETING FOR MACHINE LEARNING AND RECYCLING ROBOTICS SPECIALIST RECYCLEYE ABOUT HOW AI AND ROBOTICS & AUTOMATION IS REVOLUTIONISING SORTING TECHNOLOGY.
IN WHAT WAYS DOES THE RECYCLEYE VISION AI TECHNOLOGY HELP IMPROVE THE SORTING PROCESS? Our low-cost, AI-powered Recycleye Vision system replicates the power of human vision, using advanced machine-learning algorithms to provide automatic, image-based detection of individual items in co-mingled waste streams. This allows for 100% sampling of waste streams on a quality control line, transforming collected data into a dashboard in which plant managers can monitor their composition and granularity bringing a previously impossible visibility to waste sorting. The vision system can also differentiate between classifications such as food-grade and non-food-grade plastics and packaging vs non packaging, prohibiting valuable recyclates from being downcycled. Working with the Recycleye Robotics solution, this enables the automated picking of waste in materials recovery facilities that is faster, traceable and more accurate than human pickers. THE AI SOFTWARE LOOKS PRETTY ADVANCED. CAN YOU TELL US A LITTLE BIT ABOUT HOW ADVANCED IT IS? Waste is perhaps the most complex computer vision problem – items are overlapping, crushed in many different ways and covered in dirt. Recycleye had to develop a range of new algorithms in order to achieve the incredible +95% accuracies the system now provides (some of our work academic research has been published on our WasteNet platform). We are also the first to have brought the speed of the algorithm up to 60 frames per second meaning the system will, on average, classify each item 120 times on a conveyor belt (if it makes one error it will still have the average of the other 119 times) - this also means we are the only company with large AI models that can be installed on top of optical sorters.
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AT WHAT SPEEDS IS IT CAPABLE OF ACCURATELY IDENTIFYING AND SORTING WASTE STREAMS? Recycleye Vision and Recycleye Robotics picks at around 50 picks a minute with our 6 axis robot and around 70 picks with the delta robot on display at the Fanuc open days. AND HOW DOES IT HELP OUT THOSE MANUALLY SORTING THE WASTE? The Recycleye Robotics solution can be used alongside human pickers, with a safety cage that ensures a safe distance is maintained between the hardware and people manually sorting waste. IS THE SORTING/RECYCLING INDUSTRY SLOWLY BECOMING RELIANT ON AI AND AUTOMATION BY EFFECTIVELY ALLOWING IT TO ‘DO THE DIRTY WORK’ FOR US? Automating waste sorting brings the benefit of transparency and traceability of the materials sorted. This will become increasingly important with the requirement for increased sampling with EPR. Automated waste sorting is also more accurate, faster and can run 24/7 which is more economically efficient and safe than reliance on manual pickers, a role which experiences high turnover and recruitment issues. IN WHAT WAYS HAS FANUC EXPERTISE CONTRIBUTED TO REALISING THIS TECHNOLOGY? Our robotics solution was developed in collaboration with the team at Fanuc in the UK. We now work with Fanuc on a European exclusivity basis for the manufacture and maintenance of our robotics.
However, we believe that increasingly stringent UK, European and global policies will require classification and sampling sizes to a degree only capable by AI and robotics
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