Robot-Assisted Detection and Segregation of Recyclable Waste

The escalating global waste crisis necessitates advanced solutions to enhance recycling efficiency and sustainability. This paper presents a scalable, AI-driven robotic system for automated waste segregation that combines cuttingedge image processing, deep learning techniques, and optimized hardware. The system utilizes an enhanced InceptionV3-based Convolutional Neural Network (CNN) for classifying recyclable materials (metal, plastic, paper) in real-time, achieving category-specific accuracies of $96 \%, 90 \%$, and 85%, respectively. A Dobot Magician robotic arm equipped with a custom adaptive gripper ensures precise sorting, handling varied item shapes and sizes. Robust testing under real-world scenarios demonstrates improved reliability, with a sorting accuracy of 95% and a processing speed of 1.2 seconds per item. Additionally, economic and energy efficiency analyses reveal significant cost-effectiveness and reduced environmental impact compared to manual sorting methods. This innovative approach integrates AI, robotics, and sustainability, offering a transformative framework for industrial and urban waste management.