Quantifying Serviceability: A Maintainability Framework for Tendon-Driven Robotic Hands

Tendon-driven robotic hands are widely adopted for their dexterity and affordability, yet their practical deployment is frequently hindered by repair and maintenance challenges. This article introduces serviceability as a core performance metric-alongside force, workspace, and dexterity-by adapting the concept of Mean Time to Repair (MTTR) from reliability engineering into six key performance indicators tailored for tendon-driven hands. A lightweight benchmarking protocol is proposed for evaluating these metrics in lab and classroom settings using basic tools. This framework enables robotic hands to be assessed not only by their capabilities but also by how easily they can be restored to working conditions, making them more reliable for both academic and practical use.

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Publication Details

Journal
International Journal of Science and Research (IJSR)
Published
2025-09-03
DOI
https://doi.org/10.21275/sr25929141237
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

Quantifying Serviceability: A Maintainability Framework for Tendon-Driven Robotic Hands

R. Bhatt
International Journal of Science and Research (IJSR)
Robot Manipulation and Learning
article

Quantifying Serviceability: A Maintainability Framework for Tendon-Driven Robotic Hands

R. Bhatt
article en

Abstract

Tendon-driven robotic hands are widely adopted for their dexterity and affordability, yet their practical deployment is frequently hindered by repair and maintenance challenges. This article introduces serviceability as a core performance metric-alongside force, workspace, and dexterity-by adapting the concept of Mean Time to Repair (MTTR) from reliability engineering into six key performance indicators tailored for tendon-driven hands. A lightweight benchmarking protocol is proposed for evaluating these metrics in lab and classroom settings using basic tools. This framework enables robotic hands to be assessed not only by their capabilities but also by how easily they can be restored to working conditions, making them more reliable for both academic and practical use.

International Journal of Science and Research (IJSR)
Rhine-Waal University of Applied Sciences (DE)
Openalex Percentile: Top 66%
Robot Manipulation and Learning
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