A Hybrid Control Approach for Autonomous Robotic Cars: Combining Voice, Bluetooth, and Obstacle Detection

Abstract

This paper describes a new type of control for self-driving robotic cars that combines three modes of interaction: voice control, Bluetooth communication, and real-time obstacle detection. The primary goal of this system is to improve the operational flexibility and effectiveness of self-improving vehicles, thus increasing their usability with different settings and user requirements. In this case, the user can operate the control functions like navigation and speed adjustment hands-free and give commands through voice control. Communication via Bluetooth enables connection with mobile devices where the user can log in, monitor, or even change information. Furthermore, Automatic Obstacle Detection using ultrasonic sensors improves the real-time hazard avoidance capabilities of the robotic cars which improves safety and reliability. Using these three control methods, the electric car is able to function either autonomously or with human input, while at the same time being able to respond to environmental changes and optimize performance and user satisfaction. These results suggest that hybrid control systems used in autonomous robotic car have developed further in their application scope and complexity for real world challenges.

Introduction

The rise of autonomous systems has transformed technology, enabling machines to perform tasks previously requiring human intervention. Autonomous robotic vehicles (ARVs) are increasingly important in mobility and robotics, offering safe, flexible, and interactive navigation in household, industrial, and dynamic environments. Traditional single-mode control systems (e.g., remote-controlled or preprogrammed robots) struggle with real-time adaptability and user interaction, prompting the development of hybrid control systems that combine multiple input modalities.

This study proposes a hybrid control model integrating voice recognition, Bluetooth communication, and ultrasonic obstacle detection on an Arduino-based platform.

• Voice Recognition allows hands-free, natural control, especially useful for accessibility.

• Bluetooth Communication provides reliable short-range commands from mobile devices.

• Obstacle Detection via ultrasonic sensors ensures collision avoidance in real time.

The hybrid system enables autonomous and semi-autonomous operation, dynamically switching modes based on user input and environmental conditions. Key challenges addressed include coordinating multiple inputs and prioritizing safety through intelligent logic design. The platform uses cost-effective components (Arduino Uno, HC-05 Bluetooth module, HC-SR04 ultrasonic sensor, L298N motor driver, DC motors) and is suitable for prototyping, training, and research applications.

Methodology:

• The Arduino Uno manages all subsystems, processing voice and Bluetooth commands and obstacle data.

• Commands are translated into motor actions via the L298N driver, controlling a two-motor, four-wheeled chassis.

• The system continuously monitors for obstacles; if detected within 15 cm, it overrides user commands to avoid collisions.

• Real-time mode switching allows voice or Bluetooth control while obstacle avoidance remains active.

• Power management and termination protocols ensure safe shutdown when battery is low or on user command.

Significance:
This integrated approach demonstrates a robust hybrid control framework that enhances usability, safety, and adaptability in autonomous robotic vehicles. The study contributes to multimodal human–robot interaction, accessible control systems, and the advancement of mobile robotics in dynamic, real-world environments.

Conclusion

This work successfully designed and tested a hybrid control system for self-driving electric automobiles, combining voice recognition, Bluetooth communication, and obstacle detection for robust navigation. Experimental outcomes illustrated 92% accuracy in the interpretation of voice commands, fault-free Bluetooth functioning over a range of 10 meters, and 95% success in navigating diverse indoor scenarios, proving the efficacy of the system in both user control and autonomous safety. The combination of these modalities overcomes the shortcomings of single-mode electric systems, providing an intuitive and flexible architecture. Although the system performs optimally within a controlled environment, issues like voice misrecognition in noise indicate avenues for further improvement. This research provides a foundation for scalable robotic use, with potential improvements in the future including outdoor testing, sensor fusion, and advanced speech processing to enhance performance and practicality in the real world.

References

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Copyright

Copyright © 2025 Dr. Gauri Ghule, Chaitanya Pilane, Bhaven Peddi, Vedant Pisal, Lakshaya Pillai. 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.