Soft and compliant robotic systems have the potential to interact with humans and complex environments in more sophisticated ways than rigid robots. The majority of the state-of-the art soft robots are fabricated with silicone casting. This method is able to produce robust robotic parts, yet its results are difficult to quantify and replicate. Silicone casting also limits design complexity as well as customization due to the need to make new molds. As a result, most designs are tailored for simple, individual tasks, that is, bending, gripping, and crawling. To address more complex engineering challenges, this work presents soft robots that are fabricated by using multi-material threedimensional printing. Instead of monolithic designs, we propose a pneumatic modular toolkit consisting of a bending and an extending appendage, as well as rigid building blocks. They are assembled to achieve different tasks. We show that the performance of both appendages is (1) repeatable, that is, the same internal pressure results in the same rotation or extension across multiple specimens and repetitions, and (2) predictable, that is, the respective deformations can be modeled by using finite element analysis. Using multiple instances of both building blocks, we demonstrate the versatility of this toolkit by assembling and actuating a gripper and a crawling caterpillar. The reliability of the mechanics of the building blocks and the assembled robots show that this simple toolkit can serve as a basis for the next generation of soft robots.
Reducing energy and material consumption is a priority for the construction, aerospace, and automotive industries. Shape morphing addresses these concerns by broadening the band of functionality of a structure by adapting its shape to an external stimulus, such as pressure, or an internal stimulus, such as embedded actuation. This work outlines the development of an actuator placement optimization method for overdeterminate lattice structures with the objective of achieving predetermined large shape changes accurately. The deformation is modeled with both a linear and a nonlinear force method to determine their validity for large-shape change and their usefulness for the field of shape morphing. The linear and nonlinear methods are compared in four benchmark problems and two case studies relevant to the field of shape morphing. The nonlinear method is shown to achieve a level of accuracy $$10^2$$ 10 2 to $$10^4$$ 10 4 higher compared to FEM simulation, while using 23% fewer actuators and up to 77.3% less elongation of actuators, which makes it more favorable for shape-morphing applications. Two case studies for applications in aerospace and construction show that the nonlinear force method is better equipped for large shape change in overdeterminate meshed freeform target shapes and doubly curved surfaces with a high variable density. However, the nonlinear force method is less computationally efficient than the linear force method, as expected. A judicious choice of constraints can help reduce the run time.
Shape morphing structures are actively used in the aerospace and automotive industry. By adapting their shape to a stimulus such as heat, light, or pressure, a design can be optimized to achieve a broader band of functionality over its lifetime. The quality of a structure with respect to shape-morphing can be assessed using five criteria: weight, load-carrying capacity, energy consumption, accuracy of the controlled deformation, and the range and number of achievable target shapes. This work focuses on the use of lightweight and stiff active lattice structures, where the layout of actuators within the structure determines the final deformation. It uses a statically and kinematically determinate Kagome lattice pattern that has been shown to deform the most accurately with the least energy. The use of a determinate structure justifies the implementation of a simplified deformation model. The deformation resulting from a given actuator layout can be expressed as a linear combination of the deformation of individual actuators, which are all computed in a pre-processing step and expressed with an influence matrix. The actuator layout is thus optimized for several target shapes. The linear combination model is shown to replicate FEM simulations with an average of 94.8% accuracy for all target shapes. The actuator layouts in one-level lattices are tested using a novel design for a 3D printed modular Kagome pneumatic lattice structure. The experimental results replicate the target shapes with an average accuracy of 79.9%. The resulting actuator layouts are shown to form more target shapes with a similar deformation range as similar publications.
With advances in 3D printing and digital fabrication an opportunity is presented to realize highly customized designs whose shape can change and adapt to facilitate their functionality. A computational design method to determine the configuration of 2D pneumatic shape morphing lattices using a direct search method is implemented and assessed. The method is tested using a Kagome unit cell lattice structure, which is particularly well suited for shape morphing. To achieve shape change, beams are replaced by linear actuators such as those found in pneumatic 4D printing, whose number and placement are optimized to replicate a given target shape. The actuator placement and deformation accuracy are given for four main curvature changes: linear, convex, concave and the transition from one to the other. The results are assessed in terms accuracy of deformation and computational effort. It is shown that the method proposed produces structures that can replicate complex shape changes within 1% of the desired shape. Reducing the number of actuators for robustness purposes is shown to affect the results minimally.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.