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@article{178426,
        author = {Elamaran D and Kalpana C and Rajagopal N N and Dr. Kishore Kumar A},
        title = {Smart Robotic Arm System with Visual Processing Capabilities},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8249-8254},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178426},
        abstract = {Plastic pollution is a major environmental issue, HDPE contributing largely due to its extensive use in packaging and household items. In order to fight the current manual and semi-automated inefficient sorting methods, the paper proposes an intelligent system with automation for HDPE detection and separation from mixed waste. The system uses the YOLOv8 deep learning model to detect HDPE materials in real-time through a camera input. After detection, socket communication commands are sent to a Raspberry Pi to control the robotic arm through an Adafruit PCA9685 servo driver. With MG995 and SG90 servo motors, the arm carries out detailed pick-and-place operations to separate the detected HDPE objects. Powered by a lithium battery with a buck converter to maintain voltage regulation, it is set-up for portability and efficiency. The system maintains accuracy in recognition, processing speed, and comes with adaptability to various shapes and colors of HDPE. The automated solution uplifts the efficiency level of recycling, reduces contamination, and builds smart waste management compliant to Industry 4.0 standards.},
        keywords = {HDPE Detection, YOLOv8 Object Recognition, Robotic Sorting System, Smart Waste Management, Deep Learning Automation.},
        month = {May},
        }