Design of Household Robotic Arm System to sort Recyclable Resources based on Deep Learning

As society progresses and living conditions enhance, global domestic waste generation has witnessed a significant surge., and the problem of domestic waste separation has attracted more and more attention from the viewpoint of creating a circular economy (CE) society. Advances in machine learning and robotics offer a promising solution to this problem. Particularly, the intricacies of waste classification at the household level result in high misclassification rates, leading to inefficient waste management and recycling. Our research focuses on utilizing deep learning and robotic technologies to mitigate these issues. In this paper, we aim to design a domestic waste resource classification system built on the YOLOv5 algorithm and a household robotic arm. By integrating computer vision and machine learning with the YOLOv5 target detection algorithm and precise robotic arm operations, the system can automatically identify, classify, and sort different types of waste resources. The critical challenges that the system effectively addresses include reducing manual intervention, minimizing time consumption, and improving the accuracy of household recycling classification, while promoting the development of environmental awareness. To effectively reduce manual intervention, decrease time consumption, and enhance the accuracy of household waste classification, our main contribution is the development of an integrated system for more efficient household waste resource management.