Multi-Objective Neural Network Assisted Design Optimization of Soft Fin-Ray Grippers for Enhanced Grasping Performance

• URL: http://arxiv.org/abs/2506.00494v1
• Date: Sat, 31 May 2025 10:16:58 GMT
• Title: Multi-Objective Neural Network Assisted Design Optimization of Soft Fin-Ray Grippers for Enhanced Grasping Performance
• Abstract summary: Internal structure of the Fin-Ray finger plays a significant role in its adaptability and grasping performance.<n>When the Fin-Ray finger becomes more rigid and capable of exerting higher forces, it becomes less delicate in handling objects.<n>In this study, we employ finite element method (FEM) to estimate the deflections and contact forces of the Fin-Ray, grasping cylindrical objects.
• Score: 2.7309692684728613
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Soft Fin-Ray grippers can perform delicate and careful manipulation, which has caused notable attention in different fields. These grippers can handle objects of various forms and sizes safely. The internal structure of the Fin-Ray finger plays a significant role in its adaptability and grasping performance. However, modeling the non-linear grasp force and deformation behaviors for design purposes is challenging. Moreover, when the Fin-Ray finger becomes more rigid and capable of exerting higher forces, it becomes less delicate in handling objects. The contrast between these two objectives gives rise to a multi-objective optimization problem. In this study, we employ finite element method (FEM) to estimate the deflections and contact forces of the Fin-Ray, grasping cylindrical objects. This dataset is then used to construct a multilayer perception (MLP) for prediction of the contact force and the tip displacement. The FEM dataset consists of three input and four target features. The three input features of the MLP and optimization design variables are the thickness of the front and supporting beams, the thickness of the cross beams, and the equal spacing between the cross beams. In addition, the target features are the maximum contact forces and maximum tip displacements in x- and y-directions. The magnitude of maximum contact force and magnitude of maximum tip displacement are the two objectives, showing the trade-off between force and delicate manipulation in soft Fin-Ray grippers. Furthermore, the optimized set of solutions are found using multi-objective optimal techniques. We use non-dominated sorting genetic algorithm (NSGA-II) method for this purpose. Our findings demonstrate that our methodologies can be used to improve the design and gripping performance of soft robotic grippers, helping us to choose a design not only for delicate grasping but also for high-force applications.

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