Comprehensive Survey on Gesture-Controlled Robotic Arm

Abstract

The current project is based on the development and realization of a wireless hand-controlled robotic arm. This means that the movements and actions made with a person’s hand are captured using a special glove equipped with flex sensors transmitted to a robotic arm using wireless communications. Therefore, the wireless hand motion tracking will provide an intuitive, cable-less interface to improve the robot\'s usability, increase its accuracy, and decrease operator\'s fatigue. It is expected that the robotic arm could be used for such purposes as pick-and-place tasks and other similar actions.It should be mentioned that the project shows the possibility of developing low-cost wireless gesture recognition for controlling robotic arms by utilizing existing equipment. Such a combination allows not only capturing gestures but also providing their transmission through wireless communication and their processing in order to perform certain actions using servomotors. It should be highlighted that there is room for further improving the project since more degrees of freedom, additional force sensors, or even vision-based gesture recognition can be added.

Introduction

The Wireless Hand-Controlled Robotic Arm is a low-cost robotic system designed to replicate human hand movements using wireless communication. Users wear a glove equipped with sensors that detect finger and hand motions, which are then transmitted wirelessly to a robotic arm that mimics these movements in real time. The system aims to provide intuitive, contactless control of robotic devices for applications in automation, prosthetics, education, and hazardous environments.

The literature review highlights various approaches to gesture-controlled robotic arms and prosthetic systems. Most systems use flex sensors, EMG sensors, cameras, IoT modules, Bluetooth, Wi-Fi, or augmented reality (AR) to capture human gestures and control robotic movements. Several studies demonstrate advantages such as low cost, high accuracy, wireless operation, portability, remote monitoring, and improved human–robot interaction. Advanced systems incorporate sensory feedback, closed-loop control, and machine learning to enhance performance and user experience.

However, many existing systems face challenges including sensor calibration issues, gesture recognition errors, signal noise, latency, limited movement range, environmental sensitivity, weight imbalance, poor positioning accuracy, and limited autonomy. EMG-based systems offer precise control but are vulnerable to signal inconsistencies, while vision-based systems are affected by lighting and occlusion. IoT and AR-based solutions improve connectivity and learning capabilities but often suffer from scalability and real-world implementation challenges.

Conclusion

From primitive EMG-based gesture identification technology to modern IoT-enabled robotic arms which utilize flex sensors, MPU6050 Inertial Measurement Unit (IMU) and communication protocols such as MQTT, there has been a lot of development towards improving interactions of humans with robotic arms for applications in teleoperation, medical assistance, and industrial automation. This review outlines the developments in the field, where researchers have attempted to solve problems related to latency, noise in the signals, and power consumption through incorporation of machine learning methods. Modern technology has made it possible to simulate the complex movement of the hand and improve their performance by increasing the number of degrees of freedom of the robotic arm. It is crucial in future researches to focus on hybrid AI-enabled control, use of edge computing, and biocompatibility in robotic prosthetics.

References

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Copyright

Copyright © 2026 Aishwarya N Naik, Ashritha K A, Bhavana B L, Dimpana R, Sushma D K. 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.