Intelligent Autonomous Mobile Manipulator for Metal Recycling Applications with Automatic Identification of Target Objects Via Image Deep Learning Methods

The growing emphasis on sustainable waste management has increased the need for intelligent robotic systems capable of autonomously identifying and sorting recyclable materials. This paper presents a hybrid control framework for an autonomous mobile manipulator specifically designed for metal recycling applications. The proposed system integrates a deep learning–based perception module with a robust hybrid control strategy to enable efficient and precise handling of target objects in unstructured environments. A convolutional neural network (You Only Look Once, version 8 nano) is employed for real-time classification and localization of metal components, such as aluminum, allowing accurate target recognition under variable lighting and cluttered backgrounds. The control framework combines model-based trajectory planning with complementary fuzzy controllers and adaptive feedback mechanisms to ensure stable and responsive manipulation during object grasping and placement. The proposed architecture enhances adaptability to uncertainties in object position, shape, and orientation, which are common in real-world recycling scenarios. The system was validated through extensive experiments conducted in a simulated recycling environment and tested under real-world conditions. Performance metrics such as classification accuracy, grasping success rate, sorting efficiency, and task completion time were used to evaluate system effectiveness. Results demonstrate a significant improvement in autonomous sorting performance, with enhanced operational reliability and reduced reliance on manual labor. This research highlights the potential of intelligent robotic systems to transform industrial recycling by improving efficiency, reducing occupational hazards, and supporting sustainable development goals. The proposed framework offers a scalable solution for deploying autonomous robots in complex and dynamic waste management environments.