Transhumeral Sensory System to Control Robotic Arm

Humanoid behavior of robots has resulted in significant development in many fields, including medical science and automation. Understanding Human motions and movements plays a crucial role in designing such humanoid robots. Technological research development can apply human actions in significant fi...

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Bibliographic Details
Published in2023 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) pp. 185 - 190
Main Authors S, Bavesh Ram, A, Gokulraj V, M, Chirranjeavi, S, Aaruran, E, Harikumar M
Format Conference Proceeding
LanguageEnglish
Published IEEE 13.10.2023
Subjects
arm movement recognition
Arms
co-bot
flex sensor
human-machine interaction
Humanoid robots
Manipulators
Program processors
robot
Robot sensing systems
robotic arm
Sensor systems
Sensory band
Very large scale integration
wearable sensors
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Summary:Humanoid behavior of robots has resulted in significant development in many fields, including medical science and automation. Understanding Human motions and movements plays a crucial role in designing such humanoid robots. Technological research development can apply human actions in significant fields like humanoid robots, medical analysis and assistance systems, and sports analytics. This paper presents a sensory system that interacts and reads the movement and orientation of individual arms and is used to control robotic arms. The proposed system is a 7 Degrees of Freedom (DoF) sensory system with a 7 DoF robotic arm miming the human transhumeral arm. A wearable sensor plays a vital role as the data collected can be used in numerous ways, from training robots and other modules, to analyzing the performance of sports personnel. The sensory band collects data to provide each joint's rotation angle. The sensory system was built using IMU sensors, flex sensors, and a potentiometer with Arduino Uno as the primary controller. The acquired sensory data was communicated to a robotic arm using an RF transceiver setup. This system can be inexpensive and easily used in various fields, including sports analytics in rural areas. The experimental results are promising, providing a linear connection between the sensory system and the robotic arm with average error for the joints below 2%. Incorporating higher-range sensors can improve the performance of the proposed model.
DOI:10.1109/DISCOVER58830.2023.10316662