Development of a Low-cost Data Glove using Flex Sensors for the Robot Hand Teleoperation

Zhu, Shuo; Stuttaford-Fowler, Angus; Fahmy, Ashraf; Li, Chunxu; Sienz, Johann

doi:10.1109/isrimt53730.2021.9596972

Cited by 7 publications

(6 citation statements)

References 6 publications

(8 reference statements)

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“…B T E is the transformation matrix of Lite6 base coordinates and tool coordinates. According to the coordinate transformation relationship, it can be known that the transformation relationship between the reference plane of the Lite6 flange and the reference plane of the palm is shown in formula (5), where H T E is unchanged, that is, when the palm position changes, the center point of Lite6's end flange should also make corresponding movement to ensure that the H T E is a fixed value:…”

Section: Manipulator Coordinates Transformation and Gripper Controlmentioning

confidence: 99%

“…(2) The smart glove recognition method. [5][6][7][8] Operators wear smart gloves equipped with flexible sensors, and the sensors transmit the detected pressure and strain signals to the controller for processing to control the equipment. This type of smart glove can detect many gestures, such as VIRDYN MHand, SmartGlove, SenseGlove Nova, etc., but these gloves are often equipped with various sensors, signal transmission devices, and even control boards.…”

Section: Introductionmentioning

confidence: 99%

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An interactive gesture control system for collaborative manipulator based on Leap Motion Controller

Li,

Lou,

Cai

et al. 2024

Advances in Mechanical Engineering

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Gesture control is often used for the control of robotic manipulators. However conventional methods always focus on position control of robotic manipulators and use a fixed position to place gesture detection devices, which limits the flexibility to control and interact. This paper presents an interactive gesture control system based on Leap Motion Controller that can overcome these shortcomings. A coordinate transformation is performed between the position of the left palm detected by Leap Motion Controller and the tool center point (TCP) of the collaborative manipulator to obtain gesture data first. This gesture data is used to control the posture of a six-joint collaborative manipulator-Lite6. The position of gripper is controlled by grip level of the right palm. A controller is designed to manipulate a lift table on which Leap Motion Controller is placed to achieve adaptive ascending and descending movements to meet the needs of operators of different heights and arm spans. A modified Kalman filter is used to filter noise and smooth the signal captured by Leap Motion Controller. The experiments demonstrate that the system can operate stably and accurately control the position, posture, and gripper position of a collaborative manipulator in real time using human palms’ gestures.

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Section: Manipulator Coordinates Transformation and Gripper Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An interactive gesture control system for collaborative manipulator based on Leap Motion Controller

Li,

Lou,

Cai

et al. 2024

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…Pathfinding can be performed in outdoor areas with the support of a global positioning system (GPS), while interior mapping is required for indoor regions [7]. S. Zhu et al focus on the control of a simulated robot hand using a data glove, which gathers information from resistors that measure the flexion of the user's fingers [8]. J. Buzzatto et al, in their paper, propose a novel design of an agile, coaxial, omnidirectional rotor module that can move and exert forces in all directions without limitations on orientation and wiring.…”

Section: Introductionmentioning

confidence: 99%

A Simulated 3D Printer in CoppeliaSim

Chakraborty

Aithal

2022

IJAEML

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Purpose: Now, 3D Printer is becoming popular. Not only used for printing toys but also to print some needful items for prototyping. We go for final production after prototype approval. So the researcher needs a 3D printer for their research. A good 3D printer is not only costly but cumbersome also. Carrying a 3D printer is too much problem. We can use the simulator to continue our research work in this scenario. Here we provide the procedure for building a 3D printer in the simulator. We used Coppeliasim to make it. It is an excellent open-source simulator. It has a good user interface. Following the step, the researcher can build it quickly. All project code and simulator files are available for the researcher in Github. They can download, use and integrate into their project without any issues. Design/Methodology/Approach: In the CoppeliaSim simulator, we created a 3D printer model. We adopted the Ender 3D printer specification, a popular and economical 3D printer. After completing the model, we add the LUA script associated with the base. Some code is kept as a module inside the project folder for better understanding. After running the simulation, it will draw a square on the work area for the working demonstration. Findings/Result: We can create a virtual 3D printer through this research work. So without having an actual printer, we can test our written algorithm, try the 3D model file, and understand the functionality of the 3D Printer. It can be integrated into the researcher’s project by customizing some physical parameters or making a different 3D printer model following the procedure described here. Originality/Value: This research work may add value to the researcher’s work. We provide the information on creating a workable 3D printer as simple as possible in the CoppeliaSim robot simulator. Customizing some physical parameters can be a tool to help the research work rapidly. Paper Type: Simulation-based Research.

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“…Conveying messages in sign language through hand gestures has attracted the attention of many researchers and technology developers. As a consequence, many hand gesture recognition approaches have been proposed, such as using the change of radar waves when the user changes the state of the hand [10][11]25], utilizing image information [12][13][14], or sensors physically attached to the hand [15][16][17]. The radar-based method [10] is easy and convenient for end-users because of independent equipment but it is dramatically affected by environmental noises and the number of antennas [27].…”

Section: Introductionmentioning

confidence: 99%

“…Hand-mounted sensors were applied to measure the change in the hand shapes and hand movements that were quite efficient because of their accuracy and real-time response. Many solutions have been proposed, such as the electronic gloves [17][18]. These gloves have integrated flex sensors, mounted on the fingers, to collect the hand's curvature changing.…”

Section: Introductionmentioning

confidence: 99%

Fusion Machine Learning Strategies for Multi-modal Sensor-based Hand Gesture Recognition

Doan

Nguyen

2022

Eng. Technol. Appl. Sci. Res.

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Hand gesture recognition has attracted the attention of many scientists, because of its high applicability in fields such as sign language expression and human machine interaction. Many approaches have been deployed to detect and recognize hand gestures, like wearable devices, image information, and/or a combination of sensors and computer vision. However, the method of using wearable sensors brings much higher accuracy and is less affected by occlusion, lighting conditions, and complex background. Existing solutions separately utilize sensor information and/or only use sensor information processing and decision-making algorithms over conventional threshold comparison algorithms and do not analyze data or utilize machine learning algorithms. In this paper, a multi-modal solution is proposed that combines information for measuring the curvature of the fingers and sensors for measuring angular velocity and acceleration. The provided information from the sensors is normalized and analyzed and various fusion strategies are used. Then, the most suitable algorithm for these sensor-based multiple modalities is proposed. The proposed system also analyzes the differences between gestures and actions that are almost similar but in fact, they are just normal moving gestures.

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Development of a Low-cost Data Glove using Flex Sensors for the Robot Hand Teleoperation

Cited by 7 publications

References 6 publications

An interactive gesture control system for collaborative manipulator based on Leap Motion Controller

An interactive gesture control system for collaborative manipulator based on Leap Motion Controller

A Simulated 3D Printer in CoppeliaSim

Fusion Machine Learning Strategies for Multi-modal Sensor-based Hand Gesture Recognition

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