Our project focuses on the development of a highly efficient and versatile robotic arm capable of classifying and picking up various types of waste materials for recycling purposes. The robotic arm uses advanced deep learning techniques in combination with computer vision to identify different materials such as plastics, paper, and metals. The main objective is to enhance recycling efforts and contribute to sustainable waste management by automating the waste classification process.
Project Purpose
The primary aim of our project is to design and implement a pick-and-place robotic arm capable of efficiently classifying waste materials such as plastics, paper, and metals for recycling. By automating this process, we strive to reduce landfill waste and promote a circular economy through more efficient waste sorting.
Contributors
Muhammad Burhan Ahmed
Role: Lead Engineer and Deep Learning Specialist
Oversaw the mechanical and electrical assembly, handled deep learning model training, data wrangling, and integrated the robotics control systems to ensure smooth operation of the pick-and-place robot.
Javeria Razzaq
Role: Robotic Modelling and Control Specialist
Developed the mathematical models for the robotic arm, designed control algorithms, and fine-tuned arm movements for precise pick-and-place functionality.