DEVELOPMENT OF TRASH COLLECTOR ROBOT USING ARDUINO R3 WITH IOT BASED MONITORING AND AUTOMATED CHARGING INTERFACE

Abstract

Product classification plays a crucial role in modern industries where efficiency, accuracy, and precision are essential. Traditional manual sorting systems are often slow, error-prone, and lack innovation, particularly in terms of energy sustainability and the ability to handle a variety of product types. This study focuses on the development of a mechatronics-based conveyor product classifier that utilizes ESP32 and Arduino Uno microcontrollers. The system integrates inventory monitoring through IoT, sensor-based identification, manual toggle switching, and an Automatic Transfer Switch (ATS) powered by a solar energy backup. The primary objective is to automate the sorting of products into three categories: plastic, paper, and metal, while promoting operational efficiency and environmental sustainability. The researchers employ an experimental prototyping method to construct the product classifier. The Arduino Uno microcontroller manages sensor inputs and controls the sorting operations. The conveyor system is equipped with ultrasonic sensors to detect objects, color sensors and load cells to determine material composition, and stepper motors managed by L298N motor drivers to facilitate sorting. A potentiometer allows for real-time adjustment of operational parameters, while a toggle switch provides manual override functionality for setup and maintenance. The ATS is incorporated to ensure an automatic transition to solar power during outages, supporting continuous and eco-friendly system operation. The prototype functioned successfully and achieved its intended purpose of classifying products into plastic, paper, and metal categories. The IoT-integrated inventory monitoring provided real-time tracking of the sorting process. The ATS enabled uninterrupted operation by switching to solar power during power failures. The system demonstrated effective sorting performance, adaptability to varying inputs, and energy-efficient operation, meeting the study’s objectives. The developed mechatronics classifier presents a notable innovation in automated sorting systems by combining ESP32 control, IoT monitoring, and sustainable energy features. The manual override function and adjustable settings enhanced user flexibility, making the system suitable for various industrial applications. While the prototype is fully functional, further refinement in sensor calibration and scaling is necessary. Future developments may include integrating machine learning and advanced sensing technologies to improve sorting precision. Overall, the study offers a promising model for sustainable and adaptable industrial classification systems.