Soft robots are distinguished by their flexible and adaptable, allowing them to perform tasks that are nearly impossible for rigid robots. However, controlling their configuration is challenging due to their nonlinear material response and infinite deflection degrees of freedom. A potential solution is to discretize the infinite-dimensional configuration space of soft robots into a finite but sufficiently large number of functional shapes. This study explores a co-design strategy for pneumatically actuated soft grippers with multiple encoded stable states, enabling desired functional shape and stiffness reconfiguration. An energy based analytical model for soft multistable grippers is presented, mapping the robots' infinite-dimensional configuration space into discrete stable states, allowing for prediction of the systems final state and dynamic behavior. Our approach introduces a general method to capture the soft robots' response with the lattice lumped parameters using automatic relevance determination regression, facilitating inverse co-design. The resulting computationally efficient model enables us to explore the configuration space in a tractable manner, allowing the inverse co-design of our robots by setting desired targeted positions with optimized stiffness of the set targets. This strategy offers a framework for controlling soft robots by exploiting the nonlinear mechanics of multistable structures, thus embodying mechanical intelligence into soft structures.

翻译：软体机器人以其柔性与适应性著称，使其能够执行刚性机器人几乎无法完成的任务。然而，由于其材料的非线性响应和无限的自由度，控制其构型具有挑战性。一种潜在的解决方案是将软体机器人无限维的构型空间离散化为有限但足够多的功能形状。本研究探索了一种针对具有多个编码稳定状态的气动软体夹持器的协同设计策略，使其能够实现所需的功能形状与刚度重构。本文提出了一种基于能量的软体多稳态夹持器解析模型，将机器人的无限维构型空间映射为离散的稳定状态，从而能够预测系统的最终状态与动态行为。我们的方法引入了一种通用方法，利用自动相关性判定回归，通过集总参数网格来捕捉软体机器人的响应，从而促进逆向协同设计。所得到的计算高效模型使我们能够以可处理的方式探索构型空间，通过设定具有优化刚度的目标位置来实现机器人的逆向协同设计。该策略为控制软体机器人提供了一个框架，通过利用多稳态结构的非线性力学，从而将机械智能嵌入软体结构之中。