Robotic Arm

Abstract

This project is a description of the design and implementation of a smartphone controlled, programmable robotic arm using an Arduino microcontroller and Bluetooth communication. The robotic arm is powered by servo motors, which are controlled by a HC-05 Bluetooth module, and can be controlled wirelessly through a smartphone application. The main aim of this system is to enable the users to control the robotic arm remotely for use in automation, education and prototyping. The system uses the Arduino to provide precise control of the arm’s movement, with the smartphone acting as the user interface to send real time commands. The control app on the smartphone can be used to manually operate the robotic arm or to program it to perform certain sequences of movements. These movements are then translated into servo actions using PWM signals to control the arm’s joints. This project shows the effectiveness of the application of wireless communication, embedded systems, and robotics, with a low cost and high versatility for tasks such as object manipulation and demonstration

Introduction

Electronic robotic arms are programmable mechanical devices designed to mimic human arm functions and are widely used across industries such as manufacturing, medicine, space exploration, and automation. They perform repetitive, precise, or hazardous tasks like welding, assembly, surgery, and material handling. Key components include a stable base, flexible joints (shoulder, elbow, wrist), rigid links, and an end effector (e.g., gripper or welding torch). Powered by servos or hydraulics, they use sensors and controllers to process commands, allowing manual, semi-automated, or fully automated operation.

Recent advancements incorporate artificial intelligence and machine learning to improve autonomy, adaptability, and collaboration with humans, especially in factories, hospitals, and space environments. Research also focuses on bio-inspired designs, soft robotics, advanced sensors, and energy-efficient control algorithms, as well as IoT integration for remote monitoring and control. Ethical and safety considerations are crucial for human-robot interactions.

The described methodology details building a Bluetooth-controlled robotic arm using Arduino as the main controller, PCA9685 servo driver for managing multiple servos, HC-05 Bluetooth module for wireless communication, and servo motors (SG995 and MG90S). A mobile app interfaces via Bluetooth to control servo movements in real-time, with software developed using Arduino IDE and mobile app platforms like MIT App Inventor. Calibration ensures precise control, and the system can be extended with additional features such as gesture control and sensors for enhanced functionality.

Conclusion

The Bluetooth Controlled Robotic?Arm Using Arduino, PCA9685 Servo Driver, and the HC-05 Bluetooth Module is an efficient and adaptable solution for creating automation. With the combination?of SG995 and MG90S servos, the robotic arm has flexibility for multiple applications with precision and smooth motion. Using PCA9685 Module makes servo controlling?easier by reducing the requirement for number of PWM pins on the Arduino and also ensures stable operation.The HC-05 Bluetooth module allows for wireless control of the robotic arm and its integration with a mobile app provides real time control. The Arduino based programming provides for precise positioning of the servos based on user inputs, thus making the system very responsive. When proper calibration and testing is done on the robotic arm it moves smoothly and works efficiently. The methodology given is a good way of developing robotic systems that can be later on extended to include automation and sensor feedback among other features. This project shows the role of embedded systems, wireless communication and servo control in robotics. Some possible future enhancements could be the use of AI based automation and increased mobility for more applications.

References

[1] Lee, H., & Kim, J. (2018). AI-based robotic arms in industrial automation. Journal of Robotics Research, 25(4), 312-325. [2] Smith, R., & Jones, P. (2020). API integration in robotics: Enhancing real-time control. IEEE Transactions on Automation Science, 32(2), 150-162. [3] Patel, S., & Verma, D. (2021). Robotic-assisted surgery: Advancements and challenges. International Journal of Medical Robotics, 18(1), 45-60. [4] Nguyen, L., & Tanaka, M. (2019). Machine learning in robotic arms: Adaptive movement strategies. Artificial Intelligence and Robotics, 27(3), 95-108.

Copyright

Copyright © 2025 Mr Ashish Singh, Ch Bhupendra Singh, Vibhu Tomar, Aniket Singh , Lavi . 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.