A key building block for creating a circular economy is the ability to efficiently recover waste. For recycling to be profitable the purity of the separated fractions must be very high. The aim of the project is to implement a robotised waste sorting system to complement the current commercial solutions. The objective is to improve the quality and quantity of material recovered while limiting costs and labour use. This can be achieved thanks to advanced computer vision and robot manipulation techniques. The system will consist of two main components: (i) a vision system based on Deep Learning (DL) that combines several cameras to achieve high accuracy in material recognition; (ii) a manipulator robot that will sort objects based on feedback from the vision system. Grasp planning will exploit Reinforcement Learning (RL) to learn how to handle complex situations such as singling objects from a stack or disordered flow. The goal of innovation is twofold: to develop Artificial Intelligence techniques to be able to use low-cost sensors and to make system training simple and flexible for high reconfigurability to different types of waste.
AI and Robotics for waste sorting and recycling
Bacchin A.
;Pretto A.;Menegatti E.
2023
Abstract
A key building block for creating a circular economy is the ability to efficiently recover waste. For recycling to be profitable the purity of the separated fractions must be very high. The aim of the project is to implement a robotised waste sorting system to complement the current commercial solutions. The objective is to improve the quality and quantity of material recovered while limiting costs and labour use. This can be achieved thanks to advanced computer vision and robot manipulation techniques. The system will consist of two main components: (i) a vision system based on Deep Learning (DL) that combines several cameras to achieve high accuracy in material recognition; (ii) a manipulator robot that will sort objects based on feedback from the vision system. Grasp planning will exploit Reinforcement Learning (RL) to learn how to handle complex situations such as singling objects from a stack or disordered flow. The goal of innovation is twofold: to develop Artificial Intelligence techniques to be able to use low-cost sensors and to make system training simple and flexible for high reconfigurability to different types of waste.
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