Hierarchical Performance-Based Design Optimization Framework for Soft Grippers

This paper presents a hierarchical, performance-based framework for the design optimization of multi-fingered soft grippers. To address the need for systematically defined performance indices, the framework employs a multi-objective optimization approach, structuring the optimization process into three integrated layers: Task Space, Motion Space, and Design Space. In the Task Space, performance indices are defined as core objectives, while the Motion Space interprets these into specific movement primitives. Finally, the Design Space applies parametric, topological, and field optimization techniques to refine the geometry and material distribution of the system, achieving a balanced design across key performance metrics. The framework’s layered structure enhances SG design, balancing competing design goals without predefined weight assignments, enabling flexible trade-off exploration and scalability for complex tasks.

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Publication Details

Published
2025-04-22
DOI
https://doi.org/10.1109/robosoft63089.2025.11020848
Citations
1
Primary Topic
Robotic Mechanisms and Dynamics
Type
article
Field-Weighted Citation Impact
1.21
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article

Hierarchical Performance-Based Design Optimization Framework for Soft Grippers

Hamed Rahimi Nohooji, Holger Voos
1 citations
Robotic Mechanisms and Dynamics
1.21
article

Hierarchical Performance-Based Design Optimization Framework for Soft Grippers

Hamed Rahimi Nohooji, Holger Voos
article en
1 citations

Abstract

This paper presents a hierarchical, performance-based framework for the design optimization of multi-fingered soft grippers. To address the need for systematically defined performance indices, the framework employs a multi-objective optimization approach, structuring the optimization process into three integrated layers: Task Space, Motion Space, and Design Space. In the Task Space, performance indices are defined as core objectives, while the Motion Space interprets these into specific movement primitives. Finally, the Design Space applies parametric, topological, and field optimization techniques to refine the geometry and material distribution of the system, achieving a balanced design across key performance metrics. The framework’s layered structure enhances SG design, balancing competing design goals without predefined weight assignments, enabling flexible trade-off exploration and scalability for complex tasks.

University of Luxembourg (LU)
Openalex Percentile: Top 21%
Robotic Mechanisms and Dynamics
1.21
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