Digital Filtering Techniques for Smart Robots in Pre-University STEM Education
Sabrina Francese, Xu Du, Michelle Zhu, Weitian Wang · 2025
This work-in-progress project analyzes line-following algorithms and the utilization of digital filters for smart robots in pre-university STEM education. It explores the operation of line-following techniques combined with digital signal processing methods, including arithmetic mean, amplitude limiter, median, moving average, and recursive median filters. Through comprehensive testing and assessment, this study demonstrates the impact of digital filtering on sensor data accuracy and robot performance. Markedly, the recursive median filter achieved the highest improvement, reducing average run times by 6.33% compared to the unfiltered baseline. These preliminary findings show the importance of incorporating signal processing techniques to improve the functionality and reliability of autonomous robotic systems, making this work a valuable contribution to advancing pre-university robotics education and practical applications.