Soft Robotic Grippers, Modeled Faster

This project focus on a soft-robotic maniuplator design based on non-linear multi-stable geometry. The multi-stable nature of the design allows the manipulator to remain engage even when external stimulus(ex. pressure, electrical signal, etc) is removed. This is a unique capability among soft-robotic manipulators.

Soft robotic manipulators are often modeled using Finite Element Analysis (FEA) to predict the kinematic configuration of the manipulator under various external stimuli. However, FEA is computationally expensive and slow, making it difficult to use in real-time applications. To address this issue, we developed a novel energy-based analytical model that can predict the kinematic configuration of the manipulator with high accuracy and at a fraction of the computational cost of FEA.

The Problem: The FEA Bottleneck

Simulating soft robotics presents challenges that standard computational methods struggle to process efficiently. The non-linear behavior of these grippers further complicates traditional analysis.

• Complex Mechanics: The infinite dimensionality, material non-linearity, and large deformations of soft structures complicate standard modeling and closed-loop control.
• The FEA Limitation: While highly accurate, conventional Finite Element (FE) packages require prohibitively long simulation run times to model the complexities of interacting bistable units.

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Hardware Design: 3D Printed Soft Manipulators

The design is entirely 3D printed, so fabrication is streamlined and adjustments for optimization can all be made in CAD. The novelty of employing mult-stable structures in a soft-robotic manipulator allows us to capture the many benefits of soft-robotic end-effectors(ex. compliance, adaptability, etc) while also simplifying the problem of control inherent to soft-robotics. Structurally programmed multistability opens to the possibility of open-loop control.

• Architecture: The DPG features sequential bistable domed-shaped shell elements encapsulated in a square chamber.
• Fabrication & Actuation: The grippers are manufactured using Fused Deposition Modeling (FDM) with Thermoplastic Polyurethane (TPU) 95A. They are pneumatically actuated by applying positive and negative pressure to invert the domes and induce global curvature.

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Software: The High-Speed Computational Model

To bypass the FEA bottleneck, the continuous structure is abstracted into a computationally efficient reduced-order model.

• Lumped Parameter Lattice: The DPG is represented as a 2D lattice comprising linear, torsional, and bistable springs to map local extensions and rotations to defined energy contributions.
• Non-Linear Energy Formulation: The bistable springs capture the dome's snap-through instability using the energy equation: $$E_{\mathrm{NL}}=\frac{1}{2}{k}_b{x}^2[1+(1-\alpha \left){(\frac{x}{d}-1)}^2\right]$$
• Energy Minimization: The system's total energy $$(E_{\mathrm{tot}}=\sum _{i=1}^n{E}_L+E_{\mathrm{NL}}+E_T)$$ is minimized using the Newton-Raphson algorithm to find physically feasible, locally stable configurations.

Validation and Benchmarking

The spring lattice model was strictly validated against commercial FE software (Abaqus) and experimental physical prototypes.

Applications & Future Work

This computational efficiency enables rapid, tractable searches of the configuration space for targeted design.

• Force and Position Control: By modulating input pressure to selectively invert specific dome geometries, the system achieves reliable open-loop modulation of grasping force and tip positioning without complex sensory feedback.
• Future Trajectory: Next steps involve developing forward design tools that generate DPG topologies directly from desired properties and produce tailored grasping routines.