Embodying Control in Soft Multistable Grippers from morphofunctional co-design
🤖 AI Summary
Soft robotic systems face significant challenges in configuration control and low-fidelity discretization due to material-level strong nonlinearity and infinite-dimensional configuration spaces. To address these, this work proposes a morphology–function co-design paradigm, enabling the synthesis of pneumatic soft grippers with multistable mechanical characteristics. We establish an energy-analytic mapping from the infinite-dimensional configuration space to discrete stable equilibria, facilitating programmable reconfiguration of both pose and stiffness. Introducing the first “mechanical intelligence” embodied control framework grounded in nonlinear multistable mechanics, we further propose a novel reversible co-design methodology that enables automatic structural parameter optimization driven by functional objectives. Leveraging lattice-based lumped representation, automatic relevance determination (ARD) regression, and inverse co-design algorithms, we derive a computationally efficient and physically interpretable predictive model. Experimental validation confirms precise state switching and on-demand stiffness modulation, demonstrating substantial improvements in grasping adaptability and energy efficiency over conventional soft actuators.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Control soft robots' nonlinear material response
Discretize infinite-dimensional configuration space
Co-design multistable grippers for functional reconfiguration
Innovation
Methods, ideas, or system contributions that make the work stand out.
Discretizes configuration space
Energy-based analytical model
Inverse co-design method
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