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Development of a Solid Waste Collector Robot for Cleaning in Public Areas
Desarrollo de un robot recolector de residuos sólidos para limpieza en áreas públicas
DOI:
https://doi.org/10.15446/ing.investig.116237Keywords:
IoT, Robotics, Waste Managment, Deep Learning, Mobilenet (en)IoT, Rob´otica, Gesti´on de residuos, Aprendizaje profundo, Mobilenet (es)
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Solid waste management reduces pollution, protects public health, conserves ecosystems, and promotes recycling and circular economies in a sustainable manner. This study analyzes the pollution issues caused by inefficient solid waste management in urban areas, highlighting its negative impact on the environment. In this vein, the T5R-bot collection robot was developed, designed through 3D printing and implemented with an artificial vision model for object detection based on the single shot multibox detector (SSD) and the MobileNetV2 neural network architecture. This system allows for the autonomous identification and collection of up to 12 types of debris, achieving an accuracy of 98% and a mAP of 97.81%. The methodology included the mechanical design of the robot with a rocker-bogie mechanism, ultrasonic sensors for navigation, and a robotic arm with four degrees of freedom. The model was trained with a dataset collected from 2890 images, demonstrating high efficiency in detecting and collecting waste in contaminated environments in public areas. The results confirm the viability of the robot as a tool for improving solid waste management. In addition, the future integration of segregation and adaptive learning capabilities is proposed.
La gestión de desechos sólidos reduce la contaminación, protege la salud pública, conserva los ecosistemas y fomenta el reciclaje y las economías circulares de manera sostenible. Este estudio analiza la problemática de la contaminación causada por la gestión ineficiente de desechos sólidos en áreas urbanas, destacando su impacto negativo en el medio ambiente. En este orden de ideas, se desarrolló el robot recolector T5R-bot, diseñado mediante impresión 3D e implementado con un modelo de visión artificial para la detección de objetos basado en el single shot multibox detector (SSD) y la arquitectura de red neuronal MobileNetV2. Este sistema permite la identificación y recolección autónoma de hasta 12 tipos de residuos, alcanzando una precisión del 98% y un mAP de 97.81%. La metodología incluyó el diseño mecánico del robot con un mecanismo \textit{rocker-bogie}, sensores ultrasónicos para navegación y un brazo robótico con cuatro grados de libertad. El modelo fue entrenado con un conjunto de datos recolectado a partir de 2890 imágenes, demostrando gran eficiencia en la detección y recolección de residuos en ambientes contaminados de áreas públicas. Los resultados confirman la viabilidad del robot como una herramienta para mejorar la gestión de residuos sólidos. Además, se propone la integración futura de capacidades de segregación y aprendizaje adaptativo.
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