Automated Sliding Door System Using Pir Sensor

Automated systems have become increasingly significant in modern infrastructure, particularly in enhancing convenience, accessibility, and safety. This study presents the design and implementation of an Automated Sliding Door System using an Arduino Uno microcontroller. The system utilizes a DC geared motor controlled through an L298N motor driver to enable smooth opening and closing of the sliding door. A motion detection mechanism, such as a Passive Infrared (PIR) sensor, is employed to detect the presence of a person near the entrance, triggering the automated operation of the door. Additionally, limit switches are integrated to define the maximum open and close positions, ensuring safe operation and preventing mechanical damage. The Arduino Uno serves as the central controller, processing sensor inputs and generating appropriate motor control signals. A 7.4V lithium-ion battery pack provides sufficient power to the motor driver and control circuitry. Experimental testing demonstrates that the system operates reliably, responding accurately to motion detection and stopping at predefined limits. The proposed design is cost-effective, energy-efficient, and suitable for small-scale automation applications such as residential doors, offices, and commercial establishments.

Arduino Uno, PIR Sensor, L298N Motor Driver, DC Geared Motor, Automation, Sliding Door, Motion, Technology

1. Banzi, M., & Shiloh, M. (2014). Getting Started with Arduino, Maker Media, [Google Scholar] [Crossref]

2. Monk, S. (2016). Programming Arduino: Getting Started with Sketches. McGraw-Hill. [Google Scholar] [Crossref]

3. STMicroelectronics. (n.d.). L298N Dual H-Bridge Motor Driver Datasheet. [Google Scholar] [Crossref]

4. Arduino Uno Rev3 Datasheet, Arduino.cc. https://docs.arduino.cc/hardware/uno [Google Scholar] [Crossref]

5. Drury, C. G., & Fox, J. G. (1975). Human reliability in quality control. Taylor & Francis. [Google Scholar] [Crossref]

6. Sun, D.-W. (2016). Computer Vision Technology for Food Quality Evaluation (2nd ed.). Academic Press. [Google Scholar] [Crossref]

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