Design and development of grasping behaviour control strategy for EMG-controlled robotic hand

Name: Anwar Rajawana

Organization : Prince of Songkla University. Department of Mechanical Engineering

Email : anwar_krumkrim@hotmail.com

Name: Paramin Neranon

Organization : Prince of Songkla University. Department of Mechanical Engineering

Name: Pornchai Phukpattaranont

Organization : Prince of Songkla University. Department of Electrical Engineering

Name: Kittikhun Thongpull

Organization : Prince of Songkla University. Department of Electrical Engineering

ThaSH: Robots -- Design and construction

ThaSH: Robots -- Control systems

ThaSH: Manipulators (Mechanism)

Abstract: There are several studies that have examined the control strategy during the interaction of a robotic device while grasping an object. However, there are few research papers focusing on the stabilization of multi-fingered robot hand while picking up an object. In this study, a new set of experiments have been conducted which allow a human participant perform grasping and lifting objects with various total masses to difference targets. Once the human physically grasps the object, the interaction force between human fingers and object is measured by 3D force sensors (OMD-20-SE-40N) which mounted on five fingers. In addition, the object velocity can be estimated using ultrasonic ranging sensor (HC-SR04). To approach design guidelines for grasping an object of a multi-fingered robot hand, the mathematical model of human picking up an object with the influence of variables affecting the finger human fingers force such as masses and displacement targets has been analyzed. By using a mathematical and statistical technique, Box-Behnken design, which is a type of a Response Surface Methodology (RSM) was adopted in order to model and evaluate the functional equation that can be expressed the relationship between influential factors (mass and target) and the human finger force. By applying Analysis of Variance (ANOVA) technique to statistically evaluate the relationship as mentioned, the test results have demonstrated that at least one of the input variables significantly affected the output (human finger force) at the 95 percent confidence interval. The computed number of R2 is 0.969, which means that 96.9 percent of the surface roughness parameter (or predicted finger force) estimation is meaningfully related to the input variable parameters (mass and target). Additionally, it also indicates the third-order polynomial model used to estimate human finger forces is highly reliable. Therefore, the prediction model carried out is assured to be confirmed the appropriateness of the equation and highly reliable.

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2017

Modified: 2026-02-02

Issued: 2026-02-02

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BibliograpyCitation : In King Mongkut's University of Technology North Bangkok Faculty of Engineering. The 8th TSME International Conference on Mechanical Engineering 2017 (TSME-ICoME8) (p.77-84) Bangkok : King Mongkut's University of Technology North Bangkok

eng

©copyrights King Mongkut's University of Technology North Bangkok

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