Low-Cost Robotic Arm for differently abled using Voice Recognition

Andrews, Neema; Jacob, Sherin; Thomas, Steffin Mammen; Sukumar, S.; Cherian, Reni K.

doi:10.1109/icoei.2019.8862757

Cited by 9 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many theoretical simulation studies have been carried out on inverse kinematics applications of robot arms with different degrees of freedom [9][10][11][12][13]. On the other hand, some researchers have developed a lowcost and small-scale prototype robot arm and applied inverse kinematics results [14][15][16][17][18][19][20][21][22][23][24][25]. However, in other studies, with the emergence of Internet of Things (IoT) technology, research that allows the robot to work through internet connection has begun to be discussed.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Multi-Layer Perceptron and Inverse Kinematic for RRR Robotic Arm

et al. 2024

View full text Add to dashboard Cite

In this study, the position control simulation of a 3 Degree of Freedom (3DOF) robot arm was compared with machine learning and inverse kinematic analysis separately. The considered robot arm is designed in RRR pattern. In the inverse kinematic analysis of the robot arm, the geometric approach and the analytical approach are used together. Multi-Layer Perceptron (MLP) was used as a machine learning method. Some of the coordinate data that the robot arm can reach in the working space are selected and the MLP model is trained with these data. When training was done with MLP machine learning method, the correlation coefficient (R2) was obtained as 1. Coordinates of 3 different geometric models (helix, star and daisy) that can be included in the working space are used as test data of the MLP model. These tests are simulated in 3D in MATLAB environment. The simulation results were compared with the inverse kinematics analysis data. As a result, Mean Relative Error (MRE) values for helix, star and daisy shapes were calculated as 0.0007, 0.0033 and 0.0011, respectively, in the tests performed. Mean Squared Error (MSE) values were obtained as 0.0034, 0.0065 and 0.0040, respectively. This confirms that the proposed MLP model can operate this system at the desired stability.

show abstract

Section: Introductionmentioning

confidence: 99%

A Comparison of Multi-Layer Perceptron and Inverse Kinematic for RRR Robotic Arm

et al. 2024

View full text Add to dashboard Cite

show abstract

“…The three main causes of limb loss are illnesses, birth abnormalities, and traumatic events. Other common causes are injuries sustained in motor vehicle and cruiser accidents [3]. One of the most coveted robotic and prosthetic accomplishments, as well as one of the most challenging engineering goals ever, is to replicate the human hand [4].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Smart Prosthetic Hand

Manjunath¹

2023

IJETMS

View full text Add to dashboard Cite

An overview of the design and use of a Smart Prosthetic Hand is provided in this study. This study presents an overview of computational methods for brain, gesture, and voice signals to operate a robotic arm. There are several hundred million neurons in the human brain. A prosthetic arm powered by a Brain-Computer Interface (BCI) based on electroencephalograms (EEG) is a non-invasive approach that can be a powerful aid for persons with severe disabilities in their daily lives, particularly to let them use their arm voluntarily. The brain’s EEG data is recorded by the Brainsense headset and processed by a microprocessor to move the prosthetic hand via controlling servo motors. Additionally, a glove controller that replicates the gestures is used. The method entails using flex sensors to control the arm's movements. The voice control system was created so that voice commands could be sent through Bluetooth to control prosthetic arms. With the aid of a microcontroller, all actions can be monitored by a user interface. This prosthetic arm can help patients who have amputees below the elbow. This essay's main objective is to enable people with physical disabilities to become less dependent on others for their everyday needs. This model would be very helpful in both the real world, especially for those with disabilities who are unable to use their hands, and in the classroom when employed by college students pursuing robotics as a subject. The work given here is a mini-project that is taken up as a part of the curriculum completed by electronics and communication engineering students in the second year of the electronics & communication engineering department at Dayananda Sagar College of Engineering in Bangalore.

show abstract

“…Several studies have also designed a low-cost robot arm with recognition features. As in research [14] which applies the voice recognition method to control the robot arm. Also, other recognition methods such as visual tracking [15]- [18] and gesture control [19]- [22] have been integrated into the robot arm.…”

Section: Introductionmentioning

confidence: 99%

Small-scale Robot Arm Design with Pick and Place Mission Based on Inverse Kinematics

Tahtawi

Agni

Hendrawati

2021

jrc

View full text Add to dashboard Cite

Robot arm is often used in industry with various tasks, one of which is a pick and place. Rapid prototyping of robot arms is needed to facilitate the development of many industrial tasks, especially on a laboratory scale. This study aims to design a small-scale three degree of freedom (3-DoF) robot arm for pick and place mission using the inverse kinematics method. The mechanical robotic arm system is designed using Solidworks with four servo motors as the actuator. Arduino Mega 2560 is used as a microcontroller in which the inverse kinematic method is embedded. This method is used to move the robot based on the coordinates of the destination pick and place. The test results show that the robot arm can carry out the pick and place mission according to the target coordinates given with the largest average error of about 5 cm. While the error generated between the calculation and computation results is around 3%.

show abstract

Low-Cost Robotic Arm for differently abled using Voice Recognition

Cited by 9 publications

References 7 publications

A Comparison of Multi-Layer Perceptron and Inverse Kinematic for RRR Robotic Arm

A Comparison of Multi-Layer Perceptron and Inverse Kinematic for RRR Robotic Arm

Design and Implementation of a Smart Prosthetic Hand

Small-scale Robot Arm Design with Pick and Place Mission Based on Inverse Kinematics

Contact Info

Product

Resources

About