Underactuated Finger Design: A Minimalistic Approach Toward Biomimetic Prosthetic Finger

Dexterous prosthetic hands with a simple design are the need of the hour. The limiting factor for prosthetic hands is the structure, functionality, and control of the fingers and the thumb. Traditionally, robotic fingers used in prosthetic hands are tendon-driven or have linkage-based mechanisms. While tendon-driven mechanisms provide anthropomorphic behaviour, they lack sufficient grip force. In contrast, linkage mechanisms develop sufficient grip force but at the expense of the size and weight of the overall design. Further, a linkage-based mechanism provides only shape adaptation without joint coupling. To address this gap in prosthetic finger design, we propose a novel underactuated, hybrid, and minimalistic finger mechanism to achieve simultaneous coupling and self-adaptation. The designed finger mechanism has three degrees of freedom provided by two 4-bar mechanisms in series with two degrees of actuation. The finger is actuated through a pair of inelastic tendons acting on two joints. The tendon architecture is bio-inspired and improves the finger mechanism’s anthropomorphic behaviour. A detailed kinematic and static analysis is provided to describe the finger design’s operating principle and construction. Simulations of fingertip trajectory and contact forces are also performed. Additionally, a prototype of the mechanism was 3D printed to validate the design of the finger. Grasping simulations and experiments were also performed for a typical 4-bar-based finger mechanism. The results show improved anthropomorphic motion and increased contact forces for the current finger design compared to the conventional 4-bar mechanism-based prosthetic finger.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3572241
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
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article

Underactuated Finger Design: A Minimalistic Approach Toward Biomimetic Prosthetic Finger

Varadhan SKM, Asokan Thondiyath, Vignesh Sompur
IEEE Access
Robot Manipulation and Learning
article

Underactuated Finger Design: A Minimalistic Approach Toward Biomimetic Prosthetic Finger

Varadhan SKM, Asokan Thondiyath, Vignesh Sompur
article en

Abstract

Dexterous prosthetic hands with a simple design are the need of the hour. The limiting factor for prosthetic hands is the structure, functionality, and control of the fingers and the thumb. Traditionally, robotic fingers used in prosthetic hands are tendon-driven or have linkage-based mechanisms. While tendon-driven mechanisms provide anthropomorphic behaviour, they lack sufficient grip force. In contrast, linkage mechanisms develop sufficient grip force but at the expense of the size and weight of the overall design. Further, a linkage-based mechanism provides only shape adaptation without joint coupling. To address this gap in prosthetic finger design, we propose a novel underactuated, hybrid, and minimalistic finger mechanism to achieve simultaneous coupling and self-adaptation. The designed finger mechanism has three degrees of freedom provided by two 4-bar mechanisms in series with two degrees of actuation. The finger is actuated through a pair of inelastic tendons acting on two joints. The tendon architecture is bio-inspired and improves the finger mechanism’s anthropomorphic behaviour. A detailed kinematic and static analysis is provided to describe the finger design’s operating principle and construction. Simulations of fingertip trajectory and contact forces are also performed. Additionally, a prototype of the mechanism was 3D printed to validate the design of the finger. Grasping simulations and experiments were also performed for a typical 4-bar-based finger mechanism. The results show improved anthropomorphic motion and increased contact forces for the current finger design compared to the conventional 4-bar mechanism-based prosthetic finger.

IEEE AccessVol. 13
Indian Institute of Technology Madras (IN)
Openalex Percentile: Top 88%
Robot Manipulation and Learning
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