Smart Waste Management Robot: Integrating IoT, Artificial Intelligence, SLAM, and CNN-Based Advanced Image Processing for Real-Time Trash Collection and Categorization

In response to the growing global problem of inefficient waste management, urban and rural areas face increasing waste collection, segregation, and disposal challenges. Current systems frequently lack automation, resulting in high operational costs and environmental harm from delayed waste disposal. To address these issues, this paper describes designing and implementing an AI-powered smart trash collection and segmentation robot. The robot uses Simultaneous Localization and Mapping (SLAM) for precise navigation, combining LiDAR and GPS modules to ensure real-time localization and path planning. Equipped with the proposed CNN model, the robot performs advanced image classification and segmentation, achieving a detection accuracy of 95% for trash and 97% for non-trash items. The system’s precision in detecting trash is 96%, with a recall of 97%, demonstrating its capability to operate effectively in real-world scenarios. This solution not only automates waste collection but also promotes sustainability by reducing human intervention and optimizing energy consumption. Experimental results demonstrate the system’s high accuracy in waste detection and successful navigation using SLAM, making it a promising solution for modern waste management challenges. With further improvements in handling more complex waste types and advancing its sustainability features, this system provides a promising outlook for the future of automated waste management.