Low-Cost Intelligent Robot Car with Autonomous and Manual Wireless Navigation Systems

Abstract: This research paper introduces an abstract avoidance system controlled via Bluetooth and voice commands using an Arduino board. Ultrasonic sensors detect obstacles, providing real-time data for the system. Users can remotely control the system's movements and navigate through complex environments using a mobile device connected via Bluetooth. Additionally, voice commands enhance usability and convenience. The integration of hardware components, including ultrasonic sensors, an Arduino board, and a Bluetooth module, along with algorithm development for obstacle detection and communication protocols, enables the system's functionality. This versatile system finds applications in robotics, automation, and smart environments where obstacle avoidance is crucial. By combining Bluetooth and voice control, this project offers an efficient and user-friendly solution for enhancing control and safety in various real-world scenarios.

Obstacle, Robot car, Bluetooth and voice control

1. M. A. Sadeeq, S. R. Zeebaree, R. Qashi, S. H. Ahmed, and K. Jacksi, “Internet of Things security: a survey,” in 2018 International Conference on Advanced Science and Engineering (ICOASE), 2018, pp. 162–166. [Google Scholar] [Crossref]

2. H. I. Dino and M. B. Abdulrazzaq, “Facial Expression Classification Based on SVM, KNN and MLP Classifiers,” in 2019 International Conference on Advanced Science and Engineering (ICOASE), 2019, pp. 70–75. [Google Scholar] [Crossref]

3. M. Jayakannan and J. Inbaraj, “A RECENT REVIEW ON GROWTH OF MOBILE GENERATIONS-CASE STUDY [Google Scholar] [Crossref]

4. A. M. Abdulazeez, S. R. Zeebaree, and M. A. Sadeeq, “Design and Implementation of Electronic Student Affairs System,” Acad. J. Nawroz Univ., vol. 7, no. 3, pp. 66–73, 2018. [Google Scholar] [Crossref]

5. M.W. Spong, S. Hutchinson and M. Vidyasagar, Robot Modeling and Control, 1st Edition, Jon Wiley & Sons Inc, 2005. [Google Scholar] [Crossref]

6. K. Ogata, Modern control engineering, Prentice Hall, 2009. [Google Scholar] [Crossref]

7. S. Ge Shuzhi, T. H. Lee and C. J. Harris, “Adaptive Neural Network Control of Robotic Manipulators”. World Scientific Series in Robotics and Intelligent Systems – Vol. 19, 1998. [Google Scholar] [Crossref]

8. R. E. King, “Computational Intelligence in Control Engineering”, Marcel Decker, New York, 1999. [Google Scholar] [Crossref]

9. Wang, Y.; Hou, J.; Li, C.; Wu, J.; Jiang, Y.; Liu, M.; Hung, J.Y. Ultrafast Mode Reversal Coriolis Gyroscopes. IEEE/ASME Trans. Mechatron. 2022, 27, 5969–5980. [Google Scholar] [CrossRef] [Google Scholar] [Crossref]

10. Zhang, T.; Zhao, S.; Luan, X.; Liu, F. Bayesian Inference for State-Space Models with Student-t Mixture Distributions. IEEE Trans. Cybern. 2022, 53, 4435–4445. [Google Scholar] [CrossRef] [Google Scholar] [Crossref]

11. Arduino, [en línea]. [Consulta: septiembre de 2013]. Disponible en web: http://arduino.cc/. [Google Scholar] [Crossref]

12. Wiki. "Ultra-Sonic range measurement module “[en línea]. 4 de mayo de 2013, [Consulta: September de 2013]. Disponible web: http://www.seeedstudio.com/wiki/index.php?title=Ultra_Sonic_range_measurement_module. [Google Scholar] [Crossref]

13. Wikipedia contributors, "Servomotor" [en línea]. Wikipedia, The Free Encyclopaedia. 25 de junio de 2013, [Consulta: septiembre de 2013]. Disponible en web: http://es.wikipedia.org/wiki/Servomotor [Google Scholar] [Crossref]

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