The internal structure of the Fin-Ray fingers plays a significant role in their adaptability and grasping performance. However, modeling the grasp force and deformation behavior for design purposes is challenging. 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 gives rise to a multi-objective optimization problem. We employ the finite element method to estimate the deflections and contact forces of the Fin-Ray fingers grasping cylindrical objects, generating a dataset of 120 simulations. This dataset includes three input variables: the thickness of the front and support beams, the thickness of the crossbeams, and the equal spacing between the crossbeams, which are the design variables in the optimization. This dataset is then used to construct a multilayer perceptron (MLP) with four output neurons predicting the contact force and tip displacement in two directions. The magnitudes of maximum contact force and maximum tip displacement are two optimization objectives, showing the trade-off between force and delicate manipulation. The set of solutions is found using the non-dominated sorting genetic algorithm (NSGA-II). The results of the simulations demonstrate that the proposed methodology can be used to improve the design and grasping performance of soft grippers, aiding to choose a design not only for delicate grasping but also for high-force applications.

翻译：鳍条手指的内部结构对其适应性与抓取性能具有重要影响。然而，为设计目的建立抓取力与变形行为的模型具有挑战性。当鳍条手指刚度增加且能施加更大作用力时，其操作物体的精细度会相应降低。这两者之间的矛盾构成了一个多目标优化问题。本研究采用有限元方法估算鳍条手指抓取圆柱形物体时的偏转量与接触力，生成了包含120组仿真数据的数据集。该数据集包含三个输入变量：前梁与支撑梁厚度、横梁厚度以及横梁等间距，这些变量即为优化设计中的设计变量。基于该数据集，我们构建了一个具有四个输出神经元的多层感知机（MLP），用于预测两个方向上的接触力与指尖位移。最大接触力幅值与最大指尖位移幅值作为两个优化目标，体现了作用力与精细操作之间的权衡关系。通过非支配排序遗传算法（NSGA-II）求得解集。仿真结果表明，所提出的方法能够有效改进软体夹爪的设计与抓取性能，不仅有助于选择适用于精细抓取的设计方案，也能为高负载应用场景提供设计依据。