Wearable Control Using Feedforward Neural Networks has Been used to Control the Manipulator Arm of Field and Service Robots

Fadhilah, Archit and Setiawan, J D and Ariyanto, M (2021) Wearable Control Using Feedforward Neural Networks has Been used to Control the Manipulator Arm of Field and Service Robots. In: ICITID 2021, 30 August 2021, Yogyakarta, Indonesia.

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Abstract

Controlling the manipulator arm of a Field and Service Robot (FSR) using joystick input is a time-consuming and wasteful process. Operators who must control correctly and quickly must undergo a specialized training program and devote a considerable amount of study time. In this study, wearable control devices were used to replace joysticks, and Artificial Neural Networks were employed to run the wearable control devices. Wearable control devices were used to replace joysticks in this study (ANN). The Myo Armband, which is worn on the right arm of the operator, is the wearable control device that was used in this experiment. This sensor is composed of a variety of components, including an electromyography sensor (EMG), a 3-axis accelerometer, and a 3-axis gyroscope, among others. Whenever the operator moves his or her arm, the command to move the 3D Manipulator Arm in MATLAB / Simulink will be dependent on the operator's arm and the position of the operator's arm inside the workspace. By using the Inertial Measurement Unit (IMU) sensor that is included into the Myo armband, it is possible to detect and show the location of the operator's arm and arm position. The ANN method is used in conjunction with the IMU to handle data received from the sensor. Feedforward Neural Network (FNN) processing is used. When the FNN output is used to drive the 3D Manipulator Arm model in SimMechanics, the user/operator arm angle is controlled by the user/operator arm angle.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: wearable control myo armband artificial neural network manipulator arm
Subjects: T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 12 Jan 2022 11:03
Last Modified: 12 Jan 2022 11:03
URI: https://eprints.eudl.eu/id/eprint/9536

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