Soft actuators have been developed for a variety of applications, including soft grippers, artificial muscles, wearables, tactile devices, and medical devices. In this review, we will discuss a group of chemical materials and their robotic applications in soft actuators controlled with electrical stimuli. Soft actuators provide a deformable body and allow interaction with the environment to achieve the desired actuation pattern. We will also discuss the principles of operation and functionality and focus on important real-life applications of three groups of soft actuators: ionic polymer–metal composites (IPMCs), dielectric electroactive polymers (DEAPs), and magnetorheological elastomers (MREs). This review article aims to provide researchers interested in the field of soft robotics with a guide to various state-of-the-art chemistry methods used in electrically activated soft actuators, as well as the application areas for such devices.

Rigid joint manipulators are limited in their movement and degrees of freedom (DOF), while continuum robots possess a continuous backbone that allows for free movement and multiple DOF. Continuum robots move by bending over a section, taking inspiration from biological manipulators such as tentacles and trunks. This paper presents an implementation of a forward kinematics and velocity kinematics model to describe the planar continuum robot, along with the application of reinforcement learning (RL) as a control algorithm. In this paper, we have adopted the planar constant curvature representation for the forward kinematic modeling. This choice was made due to its straightforward implementation and its potential to fill the literature gap in the field RL-based control for planar continuum robots. The intended control mechanism is achieved through the use of Deep Deterministic Policy Gradient (DDPG), a RL algorithm that is suited for learning controls in continuous action spaces. After simulating the algorithm, it was observed that the planar continuum robot can autonomously move from any initial point to any desired goal point within the task space of the robot. By analyzing the results, we wanted to recommend a future direction for research in the field of continuum robot control, specifically in the application of RL algorithms. One potential area of focus could be the integration of sensory feedback, such as vision or force sensing, to improve the robot’s ability to navigate complex environments. Additionally, exploring the use of different RL algorithms, such as Proximal Policy Optimization (PPO) or Trust Region Policy Optimization (TRPO), could lead to further advancements in the field. Overall, this paper demonstrates the potential for RL-based control of continuum robots and highlights the importance of continued research in this area.

This paper presents a control-oriented modelling of a circular dielectric elastomer actuator loaded with a mass. Precise dynamic position control of these actuators is a challenge, because of the high level of nonlinearities. Our model takes into account nonlinear mechanical phenomena such as hyperelasticity and viscoelasticity. The behavior of dielectric elastomer actuators is analyzed by a series of experiments on three actuators with different parameters. Furthermore, the model parameters are found using optimization procedures. To improve the performance of the optimization, the steady state solution is found using the Harmonic Balance Method. Compared to a forward integration method, the time gain of the Harmonic Balance Method is significant, and exceeds 2 orders of magnitude when 6 or less harmonics are considered. The application of steady state solver enables taking the frequency response into account for the parameter identification procedure. The results obtained from the model are compared with experiments and show an excellent agreement.

Controlling flexible and continuously structured continuum robots is a challenging task in the field of robotics and control systems. This study explores the use of reinforcement learning (RL) algorithms in controlling a three-section planar continuum robot. The study aims to investigate the impact of various reward functions on the performance of the RL algorithm. The RL algorithm utilized in this study is the Deep Deterministic Policy Gradient (DDPG), which can be applied to both continuous-state and continuous-action problems. The study’s findings reveal that the design of the RL environment, including the selection of reward functions, significantly influences the performance of the RL algorithm. The study provides significant information on the design of RL environments for the control of continuum robots, which may be valuable to researchers and practitioners in the field of robotics and control systems.

As ionic polymer metal composites (IPMC) are increasingly applied to mechatronic systems, many new IPMC modeling efforts have been reported in the literature. The demands of rapidly growing technology has generated interest in advancing the intrinsic actuation and sensing capabilities of IPMC. Classical IPMC applications need constant hydration to operate. On the other hand, for IPMCs operating in air, the water content of the polymer varies with the humidity level of the ambient environment, which leads to its strong humidity-dependent behavior. Furthermore, decreasing water content over time plays a crucial role in the effectiveness of IPMC. Therefore, the primary challenge of this work is to accurately model this phenomenon. The principal contribution of the paper is a new IPMC model, which considers the change of moisture content. A novel nonlinear adaptive observer is designed to determine the unknown electric potential and humidity level in the polymer membrane. This approach effectively determines the moisture content of the IPMC during long-term continuous operation in air. This subsequently allows us to develop an effective back-stepping control algorithm that considers varying moisture content. Data from experiments are presented to support the effectiveness of the observation process, which is shown in illustrative examples.

Dielectric electroactive polymers (DEAPs) undergo large deformations when subject to an electric field, which make them an attractive material for use in novel actuator systems. This article presents the possibility of using DEAPs to model an innovative pumping actuator structure. The model was used to map important object parameters at individual operating points of the modeled pump. The experimental work involved designing the membrane and testing its changes in elasticity under the influence of varying forces and voltage supplies. In the further part of the work, a finite element model (FEM) of a pumping device was implemented. In the new construction of the pump, pressure is generated by membrane deformation. This is due to electrostatic compressive force between two electrodes applied to the polymer surface and forces generated by permanent magnets. The results are presented graphically, confirming the compliance of the model with the measurements.