The Recent Advancements in Humanoid Robot Technology

Ahirwar, Deepa; Purohit, Jahanvi; Semwal, Vijay Bhaskar; Gawre, Suresh Kumar; Rajpurohit, Meenakshi

doi:10.1109/sceecs54111.2022.9740828

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…The goal is to develop robots that not only mimic human physical characteristics but also possess cognitive abilities, creating a holistic human-like artificial specialist. The field of humanoid robotics aims to comprehend and replicate complex interactions between robots, their environment, and humans [1]. Hassan, H.F.; Abou-Loukh, S.J., Ibraheem, try to develop a real-time control system for a 5-degree-offreedom Aidee pen ROT3U robotic arm using surface electromyography (EMG) signals obtained from a wireless Myo gesture armband.…”

Section: Literature Surveymentioning

confidence: 99%

Bionic Forearm using Arduino

2024

IJFMR

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The robotic arm, a sophisticated mechanical device similar to a bionic or prosthetic limb, emulates human arm functions across industries like industry, medicine, and space exploration. Its key components—controller, actuators, end effector—endow it with precise, efficient movement essential for diverse tasks. The controller's structure facilitates seamless motion, while actuators powered by electric or hydraulic systems enable varied actions. The end effector, at the arm's end, interacts with objects, broadening its operational capabilities. Overall, robotic arms epitomize precision and adaptability, proving invaluable in sectors requiring intricate manipulation and task execution.

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Section: Literature Surveymentioning

confidence: 99%

Bionic Forearm using Arduino

2024

IJFMR

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Section: Introductionmentioning

confidence: 99%

“…In these, the researchers used some Machine Learning algorithm to classify the signals. However, it is still possible to highlight other works that could use ANN for classification of biopotential signals with applications in robotic prostheses as in (Ahirwar et al, 2022; Bijalwan et al, 2021; Challa et al, 2021; Patil et al, 2019; Semwal et al, 2021; Semwal et al, 2022). For this work, the following contributions were made: Conception of a low‐cost prototype that performs movement in a robotic arm prosthesis. System integration with acquisition subsystems, information processing, artificial neural network training, cloud storage and application with graphical user interface. Final prototype with the ability to correctly classify the largest number of gestures performed by different subjects under analysis. …”

Section: Introductionmentioning

confidence: 99%

Development of intelligent and integrated technology for pattern recognition in EMG signals for robotic prosthesis command

Barbosa

Neto

et al. 2022

Expert Systems

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Prostheses play an important role in the rehabilitation of people who have suffered some type of amputation. However, due to its high‐cost and high complexity in performing movements of everyday tasks, users of these prostheses may encounter many difficulties. Therefore, this work proposes the development of a future artificial intelligence technology based on a low‐cost functional prosthesis prototype (manufactured in a 3D printer). In the present work, we describe an intelligent system that uses an artificial neural network to recognize patterns in muscle biopotential signals in order to control a prosthesis prototype in real time. Such a system is divided into three parts: the first that performs a human–machine integration through a graphical user interface; the second that performs the signal acquisition; the third that performs the training and generalization steps of the artificial neural network. The developed interface runs on a web application that has a database hosted in the cloud and in it the system user can: Acquisition of electromyography signals; Training phase of the artificial neural network; Sends the matrix of weights of the trained network to the microcontroller; Activates in the microcontroller, the state of action of the commands from the identified gestures. To compose the results of the present work, a search was initially carried out for the ideal parameters of the artificial neural network through signals obtained from 20 volunteers. In this step, it was possible to identify the topology that best classifies the signals of each gesture, as well as the investigation of the number of neurons in the hidden layer that causes a low generalization power due to overfitting. At the end of the project, it was possible to validate the use of the system with 15 new volunteers, and it was observed that in most cases, the performance of the commands in the prosthesis prototype were performed correctly. In addition, a project cost analysis was carried out, and it was possible to verify that the prototype developed is viable and has an affordable cost in relation to the Brazilian cost of living standards. In this way, the objective of the present work is in the development of a low cost artificial intelligence technology. Such a system is equipped with an algorithm based on neural networks that can deal with different muscle biopotential signals, in order to command a robotic prosthesis.

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