Electroadhesion (EA), as an electrostatically driven, controllable adhesion technology, has unique attributes such as low noise, robust adaptability, and energy efficiency. However, its adhesion pressure is still low (0.1~10kPa) which may significantly limit its applications. This paper presents an innovative electroadhesion pad embedded with liquid and solid dielectrics. The experiments demonstrate that this liquid-solid electroadhesion pad (LSEAP) is capable of much larger adhesion pressure, compared to the traditional solid electroadhesion pad (SEAP). On one hand, the LSEAP can increase the dielectric contact with the substrate. On the other hand, the actuator can increase its dielectric strength. We also explore the application of this actuator to perching of a commercial Unmanned Aerial Vehicle (UAV), in order to promote the UAV's sustainable flight. Notably, the untethered LSEAP system, with an adhesion area as small as 4 cm2and a self-weight as light as 8.7 g, can support an UAV of 249.7 g for stable adhesion on various surfaces. The adhesion pressure generated by our LSEAD can be 32.2kPa, significantly larger than those reported in the literature. The weight ratio of the UAV to the LSEAP system is 14.6, more than double those in previous studies. The integration of this EA system markedly prolongs the operational duration of UAVs, rendering them suitable for sustainable surveillance and reconnaissance missions. This LSEAP also marks a pivotal advancement towards adhesion-based applications such as grippers and wall-climbing robots.

Soft pneumatic robots are gaining significant attention due to their compliance and adaptability in unstructured environments. While emerging dual-chamber soft pneumatic robots can achieve complex 3D deformations beyond conventional single-axis bending, real-time proprioception remains challenging due to the high degrees of freedom and the complex interaction between chambers. To address this issue, we propose a multimodal learning-based sensing method that combines camera and inertial measurement unit (IMU) and then extracts full-body shape information using deep learning algorithms. Our method enhances proprioception by effectively processing high-dimensional sensor data, providing real-time feedback on the gripper shape. The average error of key points was found to be 3.67mm (Var 8.39) for our method, while the error was 4.36mm (Var 10.47) when a camera was used alone, or 9.32mm (Var 21.29) when an IMU was used alone. Our multimodal learning-based shape estimation and reconstruction empower soft pneumatic grippers to be seamlessly integrated into the embodied AI framework, significantly improving their reliability and thus paving the way for applications in service robotics, ehabilitation robotics, and human-robot collaborations.

Soft robots and smart materials have seen rapid advancements in recent years, with significant potential applications in medical devices. Liquid crystal elastomers (LCEs) exhibit unique attributes of large deformations and diverse actuation modes, facilitating controllable bending in soft medical catheters and thereby enhancing their maneuverability during medical procedures. However, LCEs exhibit strong hysteresis, which makes their modeling and control challenging. In this paper, we develop a dynamic model of a light-stimulated LCE to describe its nonlinear time-dependent behavior. We first derive the relationship between the input laser power and the resulting temperature change of the LCE actuator, and then analyze the viscoelastic behavior by taking advantage of a spring-dashpot frame. For both the linear contraction actuator and the bending actuator, the dynamic equations can describe their behavior with acceptable errors. In the future, we will further test the LCE-based bending actuator of optimal design, and then perform real-time control of soft catheters with assistance of LCE actuators.

Soft pneumatic actuators can accomplish various customizable deformation/motion through the distribution of cavities and gradients in stiffness. However, traditional manufacturing methods, say molding, struggle to produce soft actuators with both complex cavities and desirable stiffness distributions. Regular 3D printing methods usually need extra printheads for support materials to fabricate soft actuators with cavities. In addition, the printing quality and fidelity of the whole structure cannot be uniform due to the effect of gravity, especially for a soft actuator with overhang features. To fabricate a soft actuator of uniform fidelity but desirable stiffness distributions, we propose an embedded 3D printing approach with only one active mixing printhead. By adjusting the mixing ratio of the dual-component silicone, we can achieve designated stiffness gradients, ranging from 30.2 kPa to 198 kPa. With this approach, we successfully fabricate soft pneumatic actuators with overhang features, which exhibit programmable elongation and radial expansion. Additionally, we fabricate soft bending actuators which can achieve programmable workspaces due to their predetermined stiffness distribution.

Dielectric elastomer actuators (DEAs) have been widely employed to drive various soft robots, due to their quiet fast muscle-like behavior. It is significant but challenging to model and control these soft actuators, due to their viscoelastic property, irregular geometry, complex structure, etc. In this paper, we propose a data-driven sparse identification method to discover the hidden governing equations of DEAs. These equations can help us interpret the nonlinear properties of DEAs. Due to their low computational cost, we can further use these equations to explore classic model-based control methods for real-time accurate control of viscoelastic DEAs. The experiments show that the proposed method can model the viscoelastic behavior of the DEAs with reasonable accuracy. A feedforward controller is finally developed to validate the effectiveness of the proposed method. It is expected that this modeling method can pave the way for accurate control of soft actuators/robots with structural and material nonlinearities.

Natural eyeball motions in humanoid robots can contribute to friendly communication, thus improving the human-robot interaction. In this paper, we develop antagonist-agonist artificial muscles for humanoid eyeball motions, by using dielectric elastomer actuators (DEAs). Inspired by human eyeballs, the artificial muscles consist of two pairs of DEA: one pair for the horizontal motion, and the other for the vertical motion. The fabrication time of actuators can be significantly decreased due to their simple structure. The antagonist-agonist actuator outperforms the dielectric elastomer minimum energy structure in terms of actuation displacement and response time. We conduct experiments in a lifesize human face model. The experiments demonstrate the capability of antagonist-agonist artificial muscles to mimic eyeball motions in the horizontal, vertical, and diagonal directions. Future work includes modeling and control of artificial muscles for optimal performance of various humanoid eyeball motions.

Earthworm-like soft robots have been widely studied for various applications, such as medical endoscopy and pipeline inspection. Many actuation modes have been chosen to drive the soft robots, including pneumatic actuators, dielectric elastomeric actuators, and shape memory actuators. Pneumatic actuators stand out since the soft robots with pneumatic actuation can produce relatively large forces and displacements with relatively ease of fabrication. Currently, several pneumatic actuators are used to realize elongating movement and anchoring movement of the earthworm for peristaltic motion. More pneumatic actuators not only require more pumps and valves to actuate and control the earthworm, but also lead to less efficient movement control of the earthworm. To address this issue, a new design with integrated single pneumatic actuator and cellular structures is developed to realize elongating movement and anchoring movement of the earthworm-like soft robot in peristaltic motion. With the new design, the simulation model of the new earthworm is developed to simulate both elongating and anchoring movements of the earthworm. A 3D printed prototype of the earthworm-like soft robot is fabricated to validate the proposed design and simulation model. Experimental results show good agreement with the simulation in elongations of peristaltic motion as the differences between the simulated and experimental is 5.8 % in one cycle of the peristaltic motion.

Soft robots have recently evoked extensive attention due to their abilities to work effectively in unstructured environments. As an actuation technology of soft robots, dielectric elastomers exhibit many intriguing attributes such as large strain and high energy density. This work presents a novel dielectric elastomer based soft crawling robot inspired by inchworms. To fill the need of control of the soft robot, a model describing the interaction between the dielectric elastomer actuator and the environment is proposed, which takes inertia, viscoelasticity and friction into consideration. The model can well describe the robot's dynamic performances and the modelling approach used here can be extended to other dielectric elastomer actuators with complicated geometries for control purposes. The obtained model allows us to design a feedforward plus feedback control scheme for the robot to achieve desired motion. Simulation shows fast response and good tracking performances which are further confirmed by the experiments.

Soft actuators play an important role in producing motions in soft robots, and dielectric elastomers have shown great promise because of their considerable voltage-induced deformation. In particular, air-filled dielectric elastomer actuators have been well studied, where the air inside provides prestretches to improve the actuation range. This paper proposes a network of inflated dielectric elastomer actuators, interconnected via a chamber, with the advantages to be highly deformable and continuously controllable. Theoretical analyses show that the networked design is able to largely postpone the occurrence of material failures of the actuators, resulting in a large and continuous actuation range for their control. We further carried out experiments for validation, and the results were largely in line with the theoretical predictions. These findings essentially provide insight into developing networked soft actuators, for achieving large actuation capability.

Frogs are capable of multiple locomotion modes including jumping and swimming, which enables them to adapt to various environmental conditions. This paper demonstrates a frog-inspired robot, which can mimic the swimming motion of a natural frog. The robot is developed based on dielectric elastomer actuators, which exhibits muscle-like behavior such as large voltage-induced deformation, high energy density, fast response and low weight. Inspired by the webbed feet of a frog, the foot actuator of the swimming robot is able to increase its projected area by 66% when subject to high voltage. Actuation of the foot actuators can significantly improve the averaged peak thrust by 34.5%. The total mass of the two dielectric elastomer actuators is 14g which only accounts for 13% of its total mass of 108g. The measured average swimming speed for a square wave voltage of 5kV and 0.25Hz is 19mm/s for the swimming robot. Future work of the project includes optimal design and control of this soft robot.

Soft dielectric elastomer actuators (DEAs) exhibit interesting muscle-like behavior for the development of soft robots. However, it is challenging to model these soft actuators due to their material nonlinearity, nonlinear electromechanical coupling, and time-dependent viscoelastic behavior. Most recent studies on DEAs focus on issues of mechanics, physics, and material science, while much less importance is given to quantitative characterization of DEAs. In this paper, we present a detailed experimental investigation probing the voltage-induced electromechanical response of a soft DEA that is subjected to cyclic loading and propose a general constitutive modeling approach to characterize the time-dependent response, based on the principles of nonequilibrium thermodynamics. In this paper, some of the key observations are found as follows: 1) Creep exhibits the drift phenomenon, and is dominant during the first three cycles. The creep decreases over time and becomes less dominant after the first few cycles; 2) a significant amount of hysteresis is observed during all cycles and it becomes repeatable after the first few cycles; 3) the peak of the displacement is shifted from the peak of the voltage signal and occurs after it. To account for these viscoelastic phenomena, a constitutive model is developed by employing several dissipative nonequilibrium mechanisms. The quantitative comparisons of the experimental and simulation results demonstrate the effectiveness of the developed model. This modeling approach can be useful for control of a viscoelastic DEA and paves the way to emerging applications of soft robots.