An Electromagnetic Adhered Smart Duster with Sensor-Based Navigation for Automated Board Cleaning

Abstract

Whiteboard maintenance in educational and professional settings demands frequent manual intervention that repeatedly interrupts the flow of instruction. This paper presents the Smart Duster, a compact autonomous whiteboard cleaning device that addresses this gap using four tightly integrated subsystems: electromagnetic adhesion for attachment to steel-backed board surfaces, lead-screw driven linear motion for precise traversal, infrared reflectance sensing for written-content detection, and an Arduino Uno microcontroller implementing a deterministic finite-state control algorithm. The device mounts and dismounts in seconds without any permanent installation, and cleans user-selected board segments at speeds appropriate for classroom deployment. Prototype testing on an A4-sized whiteboard demonstrated complete surface coverage across all five trials, a mean adhesion force of 9.4 N against a design threshold of 8 N, sub-millimetre positional accuracy, and infrared detection reliability exceeding 96% for standard dry-erase markers. Mean cleaning time for a fully written surface was 124 seconds, and power consumption remained at 28 W throughout. All defined performance requirements were met across 50 consecutive endurance cycles without hardware failure.

Introduction

The paper presents the Smart Duster, an autonomous whiteboard cleaning system designed to reduce the time, effort, and interruptions caused by manual board cleaning in classrooms and seminar halls. Traditional cleaning methods often leave ghost marks, require repeated effort, and reduce teaching efficiency. To address these issues, the Smart Duster uses electromagnetic adhesion to attach to steel-backed whiteboards, a stepper motor with a lead-screw mechanism for movement, TCRT5000 infrared sensors to detect writing and board edges, and an Arduino Uno to control the system through a finite-state machine.

The system operates by attaching itself to the whiteboard, scanning for written content, adjusting cleaning speed over marked areas, detecting board edges, and cleaning the selected board section automatically before detaching. It uses six operating states—Idle, Attach, Sweep, Edge Detection, Step Down, and Complete—to ensure reliable and efficient operation.

Experimental testing demonstrated strong performance. The Smart Duster achieved an average cleaning time of 124 seconds, an adhesion force of 9.4 N, 0.12 mm positioning accuracy, over 96% infrared detection reliability for standard marker colors, 100% surface coverage, and completed 50 continuous cleaning cycles without failure. The system consumed only 28 W of power and approximately 0.97 Wh per cleaning cycle, making it energy efficient.

The Smart Duster offers several advantages, including automatic operation without permanent installation, segment-wise cleaning to reduce unnecessary movement, low cost, and an open-source control architecture. However, it is limited to whiteboards with ferromagnetic steel backing, requires manual sensor calibration for light-colored markers, lacks obstacle detection, and relies on open-loop motor control.

The paper concludes that the Smart Duster provides a practical, affordable, and reliable solution for automated whiteboard cleaning in educational institutions and corporate environments. Future improvements include AI-based vision systems for better content detection, wireless smartphone control, rechargeable battery operation, closed-loop motor feedback, and support for irregular board shapes.

Conclusion

The paper presents the Smart Duster, an autonomous whiteboard cleaning system designed to reduce the time, effort, and interruptions caused by manual board cleaning in classrooms and seminar halls. Traditional cleaning methods often leave ghost marks, require repeated effort, and reduce teaching efficiency. To address these issues, the Smart Duster uses electromagnetic adhesion to attach to steel-backed whiteboards, a stepper motor with a lead-screw mechanism for movement, TCRT5000 infrared sensors to detect writing and board edges, and an Arduino Uno to control the system through a finite-state machine. The system operates by attaching itself to the whiteboard, scanning for written content, adjusting cleaning speed over marked areas, detecting board edges, and cleaning the selected board section automatically before detaching. It uses six operating states—Idle, Attach, Sweep, Edge Detection, Step Down, and Complete—to ensure reliable and efficient operation. Experimental testing demonstrated strong performance. The Smart Duster achieved an average cleaning time of 124 seconds, an adhesion force of 9.4 N, 0.12 mm positioning accuracy, over 96% infrared detection reliability for standard marker colors, 100% surface coverage, and completed 50 continuous cleaning cycles without failure. The system consumed only 28 W of power and approximately 0.97 Wh per cleaning cycle, making it energy efficient. The Smart Duster offers several advantages, including automatic operation without permanent installation, segment-wise cleaning to reduce unnecessary movement, low cost, and an open-source control architecture. However, it is limited to whiteboards with ferromagnetic steel backing, requires manual sensor calibration for light-colored markers, lacks obstacle detection, and relies on open-loop motor control. The paper concludes that the Smart Duster provides a practical, affordable, and reliable solution for automated whiteboard cleaning in educational institutions and corporate environments. Future improvements include AI-based vision systems for better content detection, wireless smartphone control, rechargeable battery operation, closed-loop motor feedback, and support for irregular board shapes.

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Copyright

Copyright © 2026 Ashwini S, Kaviya Shree S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.