Accuracy of HC-SR04 Ultrasonic Servo in Servo-Scanned 2D Ranging

Authors

  • Ayunita Haq Surabaya State University
  • Rohim Aminullah Firdaus Surabaya State University
  • Muhimmatul Khoiro Surabaya State University
  • Endah Rahmawati Surabaya State University
  • Nanang Winarto Indonesia University of Education

DOI:

https://doi.org/10.33394/j-ps.v14i3.19813

Keywords:

Ultrasonic sensor, HC-SR04, Measurement accuracy, Arduino-based ultrasonic radar

Abstract

This study evaluates the measurement accuracy of the HC-SR04 ultrasonic sensor configured as a low-cost two-dimensional servo-scanned ultrasonic ranging system using an Arduino Uno and micro-servo scanning. Unlike prior studies that typically investigate angle, distance, or material effects separately, this work presents an integrated experimental framework that simultaneously examines the combined influence of scanning angle, object distance, and surface characteristics under dynamic scanning conditions. Experiments were conducted at five angles (40°, 75°, 90°, 105°, 150°), four distances (15, 30, 45, and 60 cm), and three materials (brass, wood, plastic), with five repetitions per condition. Performance was assessed using mean absolute error (MAE) and standard deviation. The results show that measurement accuracy varies systematically with scanning geometry. The lowest errors occur near 90°, with average MAE values of approximately 0.5–0.7° for brass, 2–3° for wood, and 4–5° for plastic. At extreme angles (e.g., 150°), errors increase significantly, reaching up to ~1.5° (brass), ~5° (wood), and >8° (plastic). Across distances, MAE increases from 15 cm to 60 cm, indicating reduced accuracy at longer ranges. Material effects are also pronounced, with brass consistently yielding the lowest error and plastic the highest. These trends are consistent with the expected influence of reflection geometry and signal attenuation, although echo strength was not directly measured. Overall, reliable operation is observed within 75° < θ < 105° and 15–45 cm. These findings provide experimentally grounded insights for improving the performance of a low-cost ultrasonic servo-scanned ultrasonic ranging system in short-range applications.

References

Abdelhadi, H. (2004). Ultrasonic evaluation of surface roughness using normal incidence pulse-echo. NDT & E International, 37(6), 479–485. https://doi.org/10.1016/j.ndteint.2004.03.002

Abreu, D., Toledo, J., Codina, B., & Suárez, A. (2021). Low-cost ultrasonic range improvements for an assistive device. Sensors, 21(12), 4250. https://doi.org/10.3390/s21124250

Ali, H. S., Rahman, A. A., & Kamarudin, M. N. (2021). Analysis of error characteristics in low-cost ultrasonic distance sensors. Sensors, 21(9), 3108. https://doi.org/10.3390/s21093108

Barshan, B., & Kuc, R. (1992). Differentiating sonar reflections from corners and planar surfaces by employing an intelligent sensor. IEEE Transactions on Instrumentation and Measurement, 41(3), 368–372. https://doi.org/10.1109/19.143235

Blum, J., et al. (2010). Analysis of ultrasonic sensor accuracy for distance measurement in robotics. Measurement, 43(9), 1239–1246.

Borenstein, J., & Koren, Y. (1995). Error eliminating rapid ultrasonic firing for mobile robot obstacle avoidance. IEEE Transactions on Robotics and Automation, 11(1), 132–138.

Busaeed, S., Katib, I., Albeshri, A., Corchado, J. M., Yigitcanlar, T., & Mehmood, R. (2022). LidSonic V2. 0: A LiDAR and deep-learning-based green assistive edge device to enhance mobility for the visually impaired. Sensors, 22(19), 7435. https://doi.org/10.3390/s22197435

Carullo, A., Ferraris, F., Graziani, S., Grimaldi, U., & Parvis, M. (2002). Ultrasonic distance sensor improvement using a two-level neural network. IEEE Transactions on Instrumentation and Measurement, 45(2), 677–682.

Carullo, A., & Parvis, M. (2001). An ultrasonic sensor for distance measurement in automotive applications. IEEE Sensors Journal, 1(2), 143–147.

Chaix, J. F., Garnier, V., & Corneloup, G. (2006). Ultrasonic wave propagation in heterogeneous solid media: Theoretical analysis and experimental validation. Ultrasonics, 44(2), 200–210. https://doi.org/10.1016/j.ultras.2005.11.002

Drinkwater, B. W., & Wilcox, P. D. (2006). Ultrasonic arrays for non-destructive evaluation. NDT & E International, 39(7), 525–541.

Elfes, A. (1987). Sonar-based real-world mapping and navigation. IEEE Journal of Robotics and Automation, 3(3), 249–265.

Feng, C., et al. (2019). Performance evaluation of ultrasonic sensors for distance measurement under varying conditions. Measurement, 141, 1–10.

Firdaus, R. A., Khoiro, M., & Rahayu, V. (2022). Two-dimensional simulation of electromagnetic waves on metal materials using the FDTD method, in Journal of Physics: Conference Series, 2392, 012037.

Firdaus, R. A., Winarno, N., & Afifah, R. M. A. (2019). The effect of conductivity and permittivity on propagation and attenuation of waves using FDTD. Materials Physics and Mechanics, 42(5), 617–624.

Fox, D., Burgard, W., & Thrun, S. (1999). Markov localization for mobile robots using ultrasonic sensors. Artificial Intelligence, 114(1–2), 125–157.

Hauptmann, P., et al. (2002). Ultrasonic sensors in air: Propagation and attenuation. Ultrasonics, 40(1–8), 33–37.

Hodson, T. O. (2022). Root mean square error (RMSE) or mean absolute error (MAE): When to use them or not. Geoscientific Model Development, 15, 5481–5495. https://doi.org/10.5194/gmd-15-5481-2022

Islam, M., Lee, T. S., & Kim, S. H. (2019). Development and testing of a surface roughness measurement device based on aerial ultrasonic reflections. Paddy and Water Environment, 17(3), 485–494. https://doi.org/10.1007/s10333-019-00731-7

Kirjanów-Błażej, A., Jurdziak, L., Błażej, R., & Rzeszowska, A. (2023). Calibration procedure for ultrasonic sensors for precise thickness measurement. Measurement, 214, 112744.

Korosteleva, M., Korchagin, V. S., & Plaksin, A. V. (2025). Influence of environmental factors on the accuracy of the ultrasonic rangefinder in a mobile robotic technical vision system. Electronics, 14(7), Article 1393. https://doi.org/10.3390/electronics14071393

Kuc, R. (1997). Biologically inspired sonar recognition for mobile robots. IEEE Transactions on Instrumentation and Measurement, 46(1), 66–73.

Lee, D. K., In, J., & Lee, S. (2015). Standard deviation and standard error of the mean. Korean Journal of Anesthesiology, 68(3), 220–223.

Li, X., et al. (2018). Improving ultrasonic sensor accuracy using signal processing techniques. Measurement, 116, 1–10.

Mocanu, B., Tapu, R., & Zaharia, T. (2016). When ultrasonic sensors and computer vision join forces for efficient obstacle detection and recognition. Sensors, 16(11), 1807. https://doi.org/10.3390/s16111807

Muñoz, A., et al. (2014). Ultrasonic sensor-based distance measurement system with improved accuracy. Sensors, 14(7), 12213–12230.

Panda, K. G., Agrawal, S., & Barai, P. K. (2016). Effects of environment on the accuracy of the ultrasonic sensor operating in the millimeter range. Perspectives in Science, 8, 574–576. https://doi.org/10.1016/j.pisc.2016.06.024

Parrilla, M., et al. (2016). Environmental effects on ultrasonic sensors. Sensors, 16(12), 2102.

Peremans, H., Audenaert, K., & Van Campenhout, J. (1993). A high-resolution sensor based on tri-aural perception. IEEE Transactions on Robotics and Automation, 9(1), 36–48. https://doi.org/10.1109/70.210790

Purwanto, H., Riyadi, M., & Astuti, D. W. W. (2019). Comparison of HC-SR04 and JSN-SR04T ultrasonic sensors for distance measurement. International Journal of Electrical and Computer Engineering, 9(6), 5263–5270.

Rahman, M. A., Islam, M. S., & Hasan, M. R. (2020). Performance evaluation of an ultrasonic sensor under different environmental and operational conditions. Measurement, 149, 106987. https://doi.org/10.1016/j.measurement.2019.106987

Sabatini, R., Richardson, M. A., & Gardi, A. (2015). Airborne ultrasonic sensors for obstacle detection: Measurement uncertainty and error analysis. Measurement, 68, 345–357.

Schmidt, A., & van der Zwaan, M. (2018). Material-dependent ultrasonic reflection and its effect on distance measurement accuracy. Ultrasonics, 84, 1–10.

Shoval, S., & Borenstein, J. (2001). Using ultrasonic sensors to detect edges and corners. IEEE Transactions on Robotics and Automation, 17(2), 169–176.

Stoner, J. L., Felix, R., & Blank, A. S. (2023). Best practices for implementing experimental research methods. International Journal of Consumer Studies, 47(4), 1579–1595.

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Published

2026-06-13

How to Cite

Haq, A., Firdaus, R. A., Khoiro, M., Rahmawati, E., & Winarto, N. (2026). Accuracy of HC-SR04 Ultrasonic Servo in Servo-Scanned 2D Ranging. Prisma Sains : Jurnal Pengkajian Ilmu Dan Pembelajaran Matematika Dan IPA IKIP Mataram, 14(3), 1417–1431. https://doi.org/10.33394/j-ps.v14i3.19813

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Section

Research Articles