Liquid crystal elastomers (LCEs) are programmable materials par excellence. I review the history and state of the art of LCE materials and processing development from the perspective of the important remaining step of moving out of the academic research lab and applying LCEs as soft actuators or strain sensors. After a brief introduction for the non-expert of what LCEs are and which their main advantages and limitations are, I discuss the key breakthroughs that LCE research has undergone over its 50-year history. Building on this and drawing from fresh results from on-going research, I consider possible future development trajectories that would help address the outstanding key obstacles to reach mass production at competitive cost. I end with discussing a selected set of application scenarios with good opportunities for LCEs to perform functions that no other material could deliver. Specifically, I focus on responsive buildings incorporating LCE actuator fibres and sheets/ribbons, structural health monitoring with LCE strain sensors monitoring crack growth and propagation or alerting residents of buildings exposed to dangerous levels of deformation, and kinetic and responsive garments incorporating LCE fibre actuators and/or strain sensors.

This paper presents a study on the design and modeling of a novel pneumatic self-repairing soft actuator. The self-repairing soft actuator is composed of driving element, heating element, and repairing element. The driving element completes the deformation of the self-repairing soft actuator. The heating element and the repairing element complete the self-repairing function of the self-repairing soft actuator. A model used to optimize the structure is established, and the structure of the self-repairing soft actuator is determined through finite element analysis and experiment. The self-repairing time model of the soft actuator is established. The influences of different factors on the self-repairing effect and the self-repairing time are analyzed. The self-repairing scheme of the soft actuator is determined. Experiments show that the shortest time for the self-repairing soft actuator to complete the self-repairing process is 83 min. When the self-repairing soft actuator works normally, the bending angle can reach 129.8° and the bending force can reach 24.96 N. After repairing, the bending angle can reach 108.2°, and the bending force can reach 21.85 N. The repaired soft actuator can complete normal locomotion.

Soft robots combine the load-bearing capability of rigid material with the resilience, shape-shifting capabilities of soft materials. This paper presents a novel soft actuator with stiffness variation using particulate jamming technology. We design a hybrid composite structure consisting of driving layer and jamming layer. The driving layer with the arc air chamber aim to achieve large bending deformation. A membrane containing particles is integrated with driving layer to module its stiffness. The influence factors of stiffness variation were analyzed from energy of point of view. The dependence of granular attributes on the stiffness of the actuator was studied. Furthermore, we illustrated influence of stiffness changes on the kinematic and dynamic performance of the soft actuator. The experimental results showed these performance indexes are twofold. On the one hand, the structural parameters have significant effect on the bending angle, but on the other hand they have little effect on the end force. We found that flow resistance inside air chamber results in bending morphology variation. The dynamic response subjected to a square-wave air pressure was analyzed to exhibit the actuator’s transient and steady vibration behavior. The actuator with greater stiffness has faster responsiveness, but smaller range of motion. These conclusions are helpful to adjust the stiffness behavior and to improve motion performance.

Safe interaction and inherent compliance with soft robots have motivated the evolution of soft rehabilitation robots. Among these, soft robotic gloves are known as an effective tool for stroke rehabilitation. This research proposed a pneumatically actuated soft robotic for index finger rehabilitation. The proposed system consists of a soft bending actuator and a sensing system equipped with four inertial measurement unit sensors to generate kinematic data of the index finger. The designed sensing system can estimate the range of motion (ROM) of the finger’s joints by combining angular velocity and acceleration values with the standard Kalman filter. The sensing system is evaluated regarding repeatability and reliability through static and dynamic experiments in the first step. The root mean square error attained in static and dynamic states are 2 $^\circ$ and 3 $^\circ$ , sequentially, representing an efficient function of the fusion algorithm. In the next step, experimental models have been developed to analyze and predict a soft actuator’s behavior in free and constrained states using the sensing system’s data. Thus, parametric system identification methods, artificial neural network—multilayer perceptron (ANN-MLP), and artificial neural network—radial basis function algorithms (ANN-RBF) have been compared to achieve an optimal model. The results reveal that ANN models, particularly RBF ones, can predict the actuator behavior with reasonable accuracy in the free and constrained state (<1 $^\circ$ ). Hence, the need for intricate analytical modeling and material characterization will be eliminated, and controlling the soft actuator will be more practical. Besides, it assesses the ROM and finger functionality.

This paper presents a method to solve the kinematics of a rigid-flexible and variable-diameter continuous manipulator. The multi-segment underwater manipulator is driven by McKibben water hydraulic artificial muscle (WHAM). Considering the effect of elasticity and friction, we optimized the static mathematical model of WHAM. The kinematic model of the manipulator with load is established based on the hypothesis of piecewise constant curvature (PCC). We developed an optimization algorithm to calculate the length of the WHAMs according to the principle of minimum strain energy and obtain the configuration space parameters of the kinematic model. Based on the infinitesimal method, the homogeneous transformation matrices of the variable-diameter bending sections are computed, and the terminal position and attitude are obtained. In this paper, we studied the working space of the manipulator by quantitative analysis of the impact factors including pressure and load. A deep neural network (DNN) with six hidden layers is designed to solve inverse kinematics. The forward kinematic results are used to train and test the DNN, and the correlation coefficient between the output and target samples reaches 0.945. We carried out an underwater experiment and verified the effectiveness of the kinematic modeling and solution method.

The soft actuator is made of superelastic material and embedded flexible material. In this paper, a kind of soft tube was designed and used to assemble two kinds of pneumatic soft actuators. The experiment and finite element analysis are used to comprehensively analyze and describe the bending, elongation, and torsion deformation of the soft actuator. The results show that the two soft actuators have the best actuation performance when the inner diameter of the soft tube is 4 mm. In addition, when the twisting pitch of the torsional actuator is 24 mm, its torsional performance is optimized. Finally, a device that can be used in the production line was assembled by utilizing those soft actuators, and some operation tasks were completed. This experiment provides some insights for the development of soft actuators with more complex motions in the future.