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Dielectric elastomer actuators (DEAs) are a promising actuation technology in soft robotics owing to their large voltage-induced deformation and rapid response. However, most existing DEA design paradigms are empirical or intuitive, lacking the mathematical modeling and optimization methodology to exploit their actuation capabilities for prescribed motion tasks. In this paper, we present an automatic design methodology to maximize the concerned displacement(s) of DEAs by topology optimization of the applied spatial electric fields (SEFs). Our method is enabled by integrating the freeform SEF profile captured by implicit level sets, and the constitutive model of DEAs incorporating geometric and material nonlinearities and the electromechanical coupling effect, into a gradient-based optimizer. We implement our method for motions of single and multiple degrees of freedom (DOFs) of planar DEAs, and the optimized SEFs have been found to improve the output displacements by more than 75% compared with their intuitive counterparts. We further demonstrate a proof-of-concept application in which our designed two-DOF DEAs can actively drive various host structures to shape-morph from flat sheets to desired three dimensional configurations. Overall, our paper represents the first step toward automatic design of soft DEAs for diverse potential applications in soft machines and robots.

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Plantar pressure distribution measurement is significant for healthy monitoring and disease diagnosis. However, existing equipment lack of high-density resolution and may lead under-diagnosed of small abnormal regions. To address problem, we propose a special high resolution and low cost Baropodometry based on vision based tactile sensing (VBTS). Specially, we design a measurement platform with larger-scale sensing range (280 mm x 150 mm) and high-density resolution (440 ppi). To achieve the homogeneous illumination for the measure area, we also propose normally distributed light sources around the platform, and delicately adjust the incident angle to optimize the reconstruction performance. Besides, we design a example-based method to calibrate the platform and build a look-up table to reconstruct the plantar geometry from observed image. Experiments show the high lateral resolution that can discriminate the details (<2 mm), and the depth measurement accuracy is 68.5%. The experiments of reconstruction of plantar pressure depth map and identification of tumours show its' advantage for providing adaptive high-resolution plantar topography and pressure measurement.

Lattice structures can exhibit unusual mechanical properties by appropriate design and arrangement of the microstructures, and have already found applications in areas such as tissue engineering, stretchable electronics, and soft robotics. However, the designed microstructures generally follow simple geometric patterns with constant curvatures and theoretical models are developed for each geometry particularly. It lacks a universal method to model and design the periodic lattice constructed from microstructures with varying curvatures. In this paper, we develop a universal approach to design polygonal lattices with a wide range of microstructures with varying curvatures using instant curvature and parametric functions. A finite deformation model is proposed for the microstructures and the corresponding lattices, which considers the wavy or curled microstructure, hierarchical geometry, and large deformation. Experiments and finite-element simulations are conducted to validate the theoretical model. All of Young's modulus, stretchability, and Poisson's ratio can be inversely designed using the developed theoretical model. Results show that the stretchability of the highly stretchable lattice can be further increased by more than 4 times using densely curled microstructure. The signs of Poisson's ratio can also change with the axial strain by rationally designing the microstructures. By combining curve fitting and curvature and parametric functions, the mechanical behaviors of lattices constructed by bioinspired microstructures can also be predicted. The lattice structures are then demonstrated to reinforce a hydrogel with modulus increased by around 2 orders and act as a biomimetic soft-strain sensor. This work could aid the design of a hierarchical lattice and have potential applications in tissue engineering, stretchable electronics, and soft robotics.

Due to large shape-changing ability and high adaptability, soft crawling robots become a promising candidate in applications with unpredictable terrain and complex environments. However, designing and fabricating of soft crawling robots with hybrid soft and rigid components are still elusive. Here, we present a novel caterpillar inspired pneumatically-driven soft crawling robot, which can be directly 3D printed with multiple materials and without complex assembling process. To mimic the biological structure and morphological locomotion of caterpillars, we design the soft crawling robot with a pneumatically driven bellow-type body, 12 anisotropic frictional feet, and two end caps, and introduce a passive synergy locomotion model between the crawling robot’s body and feet. By selecting different cross-section shape of the feet, we characterize the moving performance of soft crawling robots. Finally, we integrate a pneumatic closed-loop control system to drive the soft crawling robots with a periodic gait and demonstrate their motion capability in a curve plastic tube.

Helical shapes are ubiquitous in both nature and engineering. However, the development of soft actuators and robots that mimic helical motions has been hindered primarily due to the lack of efficient modeling approaches that take into account the material anisotropy and the directional change of the external loading point. In this work, we present a theoretical framework for modeling controllable helical deformations of cable-driven, anisotropic, soft composite actuators. The framework is based on the minimum potential energy method, and its model predictions are validated by experiments, where the microarchitectures of the soft composite actuators can be precisely defined by 3D printing. We use the developed framework to investigate the effects of material and geometric parameters on helical deformations. The results show that material stiffness, volume fraction, layer thickness, and fiber orientation can be used to control the helical deformation of a soft actuator. In particular, we found that a critical fiber orientation angle exists at which the twist of the actuator changes the direction. Thus, this work can be of great importance for the design and fabrication of soft actuators with tailored deformation behavior.

For the cable-driven hyper-redundant robot, most of the current drive cable length estimation methods consider that the cable always passes through the center of the hole, and do not consider the effect of cable-hole clearance, resulting in a large motion error. In this paper, we propose a cable length estimating method by considering cable-hole clearance. Based on the simplified space model of the cable hole, it uses the recursive algorithm to obtain the approximate solution of the actual drive cable length. The algorithm can meet the requirements of real-time control while ensuring calculation accuracy. The correctness of the approximate solution is verified by using the interior point penalty function method. Taking a 24DoF hyper-redundant manipulator with joint angle closed-loop control as an example, the effectiveness of the proposed method is proved by comparing the theoretical calculation results with the actual drive cable length.

No abstract is provided for this article.

The Drosophila larva, a soft-body animal, can bend its body and roll efficiently to escape danger. However, contrary to common belief, this rolling motion is not driven by the imbalance of gravity and ground reaction forces. Through functional imaging and ablation experiments, we demonstrate that the sequential actuation of axial muscles within an appropriate range of angles is critical for generating rolling. We model the interplay between muscle contraction, hydrostatic skeleton deformation, and body-environment interactions, and systematically explain how sequential muscle actuation generates the rolling motion. Additionally, we constructed a pneumatic soft robot to mimic the larval rolling strategy, successfully validating our model. This mechanics model of soft-body rolling motion not only advances the study of related neural circuits, but also holds potential for applications in soft robotics.

No abstract is provided for this article.