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Real-Time Solid Waste Sorting Using a Vision-Enabled Robotic Platform
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DOI：10.3390/waste4020016.png)
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Abstract
En 中文
This paper describes the development of an automated solid waste sorting system that integrates advanced computer vision pipelines with a robotic manipulator for real-time classification and actuation. The system consists of a Deep Neural Network (DNN) and a YOLOv8-based perception module. Thedeveloped model is capable of accurately detecting and classifying objects with confidence scores exceeding 0.71, and the overall system attained a sorting accuracy of approximately 81.8% across multiple test batches. From an integration perspective, the coordination among the Intel RealSense camera, Raspberry Pi 5, Arduino Uno, ultrasonic sensors, relay-switching circuit, and SCORBOT-ER 4U robotic arm demonstrated reliable communication and execution, enabling accurate pick-and-place operations. Overall, the results confirm that the proposed system provides a functional and scalable proof of concept for automated waste segregation in controlled environments. The study highlights that while current performance is sufficient for low-speed applications, further improvements in dataset diversity, perception robustness, mechanical gripping, and feedback control are necessary to achieve higher accuracy, reliability, and industrial applicability.
Keywords:
waste management
neural network
sorting
microcontroller
automation
interfacing
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