Hierarchical Performance-Based Design Optimization Framework for Soft Grippers

TL;DR

This paper introduces a hierarchical, performance-based framework for optimizing multi-fingered soft grippers, integrating task, motion, and design considerations to improve performance and scalability in soft robotics.

Contribution

It proposes a novel layered optimization framework that systematically links task objectives, movement primitives, and design parameters for soft gripper development.

Findings

Enhanced design performance and scalability for complex tasks

Systematic integration of task, motion, and design layers

Improved balance of key performance metrics in soft grippers

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 structures 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 and topological 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, ensuring balanced performance and scalability for complex tasks and contributing to broader advancements in soft robotics.