Plastic collecting robot

This study presents the design and implementation of a novel plastic collecting robot employing machine learning techniques within the Google Colab environment, leveraging the YOLOv8 object detection model. Plastic pollution poses a significant threat to environmental health, and automated solutions are crucial for effective mitigation. The proposed robot integrates machine learning algorithms to autonomously identify and collect plastic waste in various environments. The YOLOv8 object detection model is utilized for real-time detection and localization of plastic objects, enabling precise navigation and efficient collection. The development process encompasses data preprocessing, model training, and integration with robotic hardware. Google Colab provides a convenient and scalable platform for collaborative development and resource-efficient training of machine learning models. The experimental results demonstrate the effectiveness and robustness of the plastic collecting robot in different scenarios, showcasing its potential for addressing plastic pollution challenges. Future work involves further optimization of the system and deployment in real-world environments to enhance its practical applicability and environmental impact mitigation capabilities.