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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM68364

IOT-BASED SMART WHEELCHAIR NAVIGATION: ENABLES AUTONOMOUS WHEELCHAIR MOVEMENT USING IOT SENSORS AND GPS

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM68364

Volume:

10 |

10 |

Oct 2026

Pages:

1-5

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM68364

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

ASHWITH R

ASHWITH R

Electronics and Communication Engineering

K Ramakrishnan College of Technology

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MAHARAJA P

MAHARAJA P

Electronics and Communication Engineering

K Ramakrishnan College of Technology

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JEEVA V

JEEVA V

Electronics and Communication Engineering

K Ramakrishnan College of Technology

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KEERTHIVASAN V

KEERTHIVASAN V

Electronics and Communication Engineering

K Ramakrishnan College of Technology

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Mentors

MANI P

MANI P

Electronics and Communication Engineering

K Ramakrishnan College of Technology

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Abstract

The increasing demand for independent mobility has encouraged the development of intelligent wheelchair systems that support people with physical disabilities and limited mobility. This paper presents an IoT-Based Smart Wheelchair Navigation system designed to support autonomous movement using IoT sensors and GPS-based location tracking. The proposed system aims to improve user mobility, reduce manual effort, and support safer navigation in indoor and outdoor environments. An ESP32 microcontroller is considered as the central processing unit because of its processing capability and wireless connectivity. Motor driver circuits and DC motors can be used to control wheelchair movement, while appropriate sensors can assist in detecting the surrounding environment. GPS can provide location information, and IoT connectivity can enable remote monitoring and status communication. The current hardware inventory includes an ESP32-WROOM-32, TB6612FNG motor driver, QTR-8RC reflectance sensor array, GA12-N20 DC motors, and a rechargeable battery. These components provide a foundation for developing and testing basic mobile robot navigation; however, dedicated GPS hardware, wheelchair integration, and complete IoT monitoring functions require further implementation and verification. The proposed approach provides a starting point for developing an affordable intelligent mobility assistance system.

KEYWORDS

ESP32 Microcontroller Smart Wheelchair Autonomous Navigation GPS Mobility Assistance Obstacle Detection.

Cite This Article

ASHWITH R, MAHARAJA P, JEEVA V, KEERTHIVASAN V, MANI P, "IOT-BASED SMART WHEELCHAIR NAVIGATION: ENABLES AUTONOMOUS WHEELCHAIR MOVEMENT USING IOT SENSORS AND GPS," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-5, Oct 2026. https://doi.org/10.55041/IJSREM68364

Full Article/Conclusion

The proposed IoT-Based Smart Wheelchair Navigation system provides a foundation for developing an intelligent mobility assistance platform by integrating embedded control, sensor-based navigation, and wireless communication. The ESP32-WROOM-32, TB6612FNG motor driver, QTR-8RC reflectance sensor array, and DC motors support the development of a basic robotic navigation prototype. The proposed system requires further development to incorporate reliable obstacle detection, GPS tracking, and a complete IoT monitoring interface. Experimental evaluation of navigation accuracy, response time, communication reliability, and battery endurance is also necessary to assess its performance. Overall, this project establishes a starting point for future smart wheelchair development, with further testing and safety validation required before practical application.

References

[1] Bakouri, M., Alqarni, A., Alanazi, S., Alassaf, A., AlMohimeed, I., & Alqahtani, T. (2025). Analysis of autonomous wheelchair navigation technologies in the past five years: A systematic review. International Journal of Online and Biomedical Engineering, 21(3), 56–83.   [2] Burhanpurkar, M., Labbe, M., Guan, C., Michaud, F., & Kelly, J. (2017). Cheap or robust? The practical realization of self-driving wheelchair technology. 2017 International Conference on Rehabilitation Robotics (ICORR), 1079–1086.   [3] Callejas-Cuervo, M., et al. (2020). Control systems and electronic instrumentation applied to autonomy in wheelchair mobility: The state of the art. Sensors, 20(21), Article 6326.   [4] Kim, E. Y. (2016). Wheelchair navigation system for disabled and elderly people. Sensors, 16(11), Article 1806.   [5] Mitsuhashi, H., & Itami, T. (2025). Fully automatic control of electric wheelchair by measuring obstacle shape using monocular camera and laser. Journal of Robotics and Mechatronics, 37(2), 523–534.   [6] Nguyen, A. V., Nguyen, L. B., Su, S., & Nguyen, H. T. (2012). Development of a Bayesian neural network to perform obstacle avoidance for an intelligent wheelchair. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1884–1887.   [7] Nguyen, A. V., Nguyen, L. B., Su, S., & Nguyen, H. T. (2013). The advancement of an obstacle avoidance Bayesian neural network for an intelligent wheelchair. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 3642–3645.   [8] Pololu Corporation. (n.d.). QTR-8A and QTR-8RC Reflectance Sensor Array User’s Guide.   [9] Pu, J., Jiang, Y., Xie, X., Chen, X., Liu, M., & Xu, S. (2018). Low cost sensor network for obstacle avoidance in share-controlled smart wheelchairs under daily scenarios. Microelectronics Reliability, 83, 180–186.   [10] Simpson, R. C. (2005). Smart wheelchairs: A literature review. Journal of Rehabilitation Research and Development, 42(4), 423–436.  

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Publication Date

10/26/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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