Robotic Applications Research Articles

A skin-inspired anisotropic multidimensional sensor based on low hysteresis organohydrogel with linear sensitivity and excellent robustness for directional perception

Human skin has complex sensors to perceive three-dimensional stimuli from the environment. Skin-inspired flexible sensors with multidimensional and directional perception are desirable for applications in intelligent robots and human–machine interaction. Herein, wearable multidimensional sensors have been fabricated by macroscopically assembling 3D-printed microstructured anisotropic organohydrogel sensory units through robust interfacial adhesion. The organohydrogel is crosslinked using microgels and starch microparticles through non-covalent interactions, and composited with short carbon fibers as conductive fillers. The physical network exhibits low hysteresis against 5000 cyclic loadings, which demonstrates excellent robustness in both mechanical properties and sensory performance. The organohydrogel precursor is printed into a cone-shaped sensor with a pressure sensitivity 50 times higher than that of its bulk counterpart. A sea cucumber epidermis-like microstructured sensor with abundant highly sensitive cone array is assembled with 3D-printed anisotropic gels with highly aligned carbon fibers inside, generating a multidimensional sensor capable of perceiving stimuli along X-, Y-, and Z-axis. The assembled sensor can directionally distinguish external forces and identify human motions. This study demonstrates a promising strategy to fabricate well-structured organohydrogel sensors for applications in electronic skin, biomimetic flexible electronics, and artificial intelligence.

Main-Chain Azobenzene Poly(ether ester) Multiblock Copolymers for Strong and Tough Light-Driven Actuators.

The stimulus-responsive polymeric materials have attracted great research interest, especially those remotely manipulated materials with potential applications in actuators and soft robotics. Here we report a photoresponsive main-chain actuator based on azobenzene poly(ether ester) multiblock copolymer (mBCP) thermoplastic elastomers, (PTAD-b-PTMO-b-PTAD)n, which were synthesized by a cascade polycondensation-coupling ring-opening polymerization method using poly(tetramethylene oxide) (PTMO) and azobenzene-containing cyclic oligoesters (COTADs) as monomers. The thermal, mechanical, and microphase separation behaviors of mBCPs could be flexibly tuned by altering the ratios of soft-to-hard segments and block number (n). The oriented azobenzene mBCP fibers were prepared by melt spinning, showing reversible photoresponsive properties with remarkably high strength (∼1000 MPa) and high elongation at break comparable to spider silks. Fast photoinduced bending and contraction were successfully achieved in these fibers with high work and power densities and energy conversion efficiency, enabling it to lift up about 250 times of its own weight. Moreover, it can take out materials inside the tube by UV-light control. These fibers could be applied in light-driven actuators or telecontrolled robot arms.

HoLLiECares - Development of a multi-functional robot for professional care.

Germany's healthcare sector suffers from a shortage of nursing staff, and robotic solutions are being explored as a means to provide quality care. While many robotic systems have already been established in various medical fields (e.g., surgical robots, logistics robots), there are only a few very specialized robotic applications in the care sector. In this work, a multi-functional robot is applied in a hospital, capable of performing activities in the areas of transport and logistics, interactive assistance, and documentation. The service robot platform HoLLiE was further developed, with a focus on implementing innovative solutions for handling non-rigid objects, motion planning for non-holonomic motions with a wheelchair, accompanying and providing haptic support to patients, optical recognition and control of movement exercises, and automated speech recognition. Furthermore, the potential of a robot platform in a nursing context was evaluated by field tests in two hospitals. The results show that a robot can take over or support certain tasks. However, it was noted that robotic tasks should be carefully selected, as robots are not able to provide empathy and affection that are often required in nursing. The remaining challenges still exist in the implementation and interaction of multi-functional capabilities, ensuring ease of use for a complex robotic system, grasping highly heterogeneous objects, and fulfilling formal and infrastructural requirements in healthcare (e.g., safety, security, and data protection).

Current research status and prospects for clinical application of robotics in spinal surgery

With the rapid advancement of artificial intelligence and the increasing integration of medical engineering, robot-assisted (RA) technology in spinal surgery has made significant strides, with its clinical application range continuously expanding. This article provides a comprehensive review of relevant literature on spinal surgery robots over the past decade, summarizing recent advancements in screw placement techniques such as cervical, thoracic, and lumbar pedicle screws; cortical bone trajectory (CBT) screws; cervical lateral mass screws; and S2 sacroiliac (S2AI) screws. It also discusses RA-guided targeted puncture and endoscope placement through intervertebral foramen as well as decompressive laminectomy procedures. Furthermore, this article systematically analyzes the advantages and disadvantages of RA technology while elucidating its accuracy, safety, and practicality. We believe that this article will help clinicians to gain a deeper understanding of the current clinical application status of spinal surgery robots while promoting further enhancements and wider adoption of RA technology to ultimately benefit more patients.

H‐PME: Development of a Robot Skin Using Halbach Array Permanent Magnet Elastomer

This article presents a novel 3‐axis Halbach permanent magnet elastomer (H‐PME) sensor for the robotic application, which effectively reduces crosstalk along when two sensors are used simultaneously in close proximity, for example, during grasping of thin and delicate objects, needle threading, etc. This sensor integrates a Halbach‐array magnetic elastomer, a 3×3 Hall sensor matrix, and a silicone layer. The magnetic elastomer is produced by combining NdFeB powders with a diameter of 5 μm into silicone, following a weight ratio of 50%, and then magnetized using 2D Halbach‐array magnets. Simulation results reveal the capability to adjust magnetic field strength and distribution by altering the magnet's orientation. The sensor's efficacy in 3‐axis sensing is validated through calibration with a linear model, achieving a good root‐mean‐square error below 0.7 N in force measurement. The H‐PME sensor, with a thickness of merely 4.5 mm, can detect forces up to 50 N. It's simple 3‐layer design allows the thickness to be reduced to as low as 2 mm, while also offering ease of replacement. Crucially, crosstalk evaluation experiments show that the proposed H‐PME sensor can dramatically mitigate crosstalk interference.

Modeling the Dynamic Response of a Light-Driven Liquid Crystal Elastomer Fiber/Baffle/Spring-Coupled System

Abstract A self-oscillating system can harness energy from the external environment to sustain its continuous motion, making it highly suitable for applications in soft robotics, military industry, energy, and other fields. This article introduces a system composed of a light-responsive liquid crystal elastomer fiber, a baffle, and a spring, capable of achieving self-oscillation under stable lighting conditions. Considering the established dynamic model of light-responsive liquid crystal elastomers (LCEs), a nonlinear dynamic model is introduced and used as the basis for exploring its dynamic characteristics. Numerical computations reveal that the coupled system exhibits two distinct motion states: self-oscillation and static states. The behavior of the system is sustained through the interaction of light energy and damping dissipation. Furthermore, a detailed investigation is conducted on the key system parameters affecting the frequency and amplitude of self-oscillation. In contrast to the complexity of current self-oscillating systems, this particular self-oscillating system features simplicity in structure, ease of manufacture, and strong adaptability. These advantages are expected to provide broader design possibilities for micro-machines and mechanical production processes.

BHT-QAOA: The Generalization of Quantum Approximate Optimization Algorithm to Solve Arbitrary Boolean Problems as Hamiltonians.

A new methodology is introduced to solve classical Boolean problems as Hamiltonians, using the quantum approximate optimization algorithm (QAOA). This methodology is termed the "Boolean-Hamiltonians Transform for QAOA" (BHT-QAOA). Because a great deal of research and studies are mainly focused on solving combinatorial optimization problems using QAOA, the BHT-QAOA adds an additional capability to QAOA to find all optimized approximated solutions for Boolean problems, by transforming such problems from Boolean oracles (in different structures) into Phase oracles, and then into the Hamiltonians of QAOA. From such a transformation, we noticed that the total utilized numbers of qubits and quantum gates are dramatically minimized for the generated Hamiltonians of QAOA. In this article, arbitrary Boolean problems are examined by successfully solving them with our BHT-QAOA, using different structures based on various logic synthesis methods, an IBM quantum computer, and a classical optimization minimizer. Accordingly, the BHT-QAOA will provide broad opportunities to solve many classical Boolean-based problems as Hamiltonians, for the practical engineering applications of several algorithms, digital synthesizers, robotics, and machine learning, just to name a few, in the hybrid classical-quantum domain.

Frontiers in construction 3D printing: self-monitoring, multi-robot, drone-assisted processes

AbstractTo overcome productivity issues and revolutionize the stagnating construction industry, a large amount of research efforts has been devoted to robot-assisted construction technology. The advancements in robotics including mechanical system design, tool design, digital system design, and numerical control systems design enabled engineers to create complex geometries that are infeasible for conventional construction methods. In addition, innovative robotic systems that utilize mobile platforms, multiple robots, and unmanned aerial vehicles have demonstrated significant promise in fully automating the construction process. This work will provide a perspective on the state-of-the-art applications of robotics in the revolution of construction, where a comprehensive review of the current development of the relevant software and hardware, 3D concrete printing (3DCP), robot-assisted assembly of discrete prefabricated blocks, real-time quality monitoring and feedback control systems, and typical innovative robot-assisted structural designs are conducted. Finally, the limitations of existing robot-assisted construction technology are identified, which leads to several recommendations for future research toward fully automatic construction.

Sulphonated-graphene oxide nanocomposite membranes with PVA-ZnO nanostructures and mechanized agitation-enhanced pt coating for soft robotics bending actuator

Ionic Polymer-Metal Composite (IPMC) actuators have garnered significant scientific attention in robotics and artificial muscles for their ability to operate at low voltage, high strain capacity, and lightweight construction. The lack of uniform bending in IPMC actuators undermines their control precision and restricts their range of potential applications. This study utilized the unique properties of nanoscale materials and Polyvinyl alcohol (PVA) to develop a membrane for soft robotic bending actuation. Subsequently, a platinum coating was applied to the membrane to mitigate the limitations of IPMCs in soft robotics applications. Herein, mechanized agitation was employed during the solution-casting process to enhance platinum metal (Pt-metal) coating on zinc oxide (ZnO) nanostructures within a PVA- sulphonated graphene oxide nanocomposite, achieving enhanced soft robotics bending actuation capabilities. The resultant membrane composed of sulphonated-graphene oxide and polyvinyl-zinc oxide coated with pt (PsGZ-Pt) was examined by exploring advanced analytical techniques such as Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray diffraction spectroscopy (XRD). Furthermore, the ionic exchange capacity (IEC), proton conductivity (PC), water uptake and water loss characteristics were evaluated to be 2.1 meq. g−1, 1.95 × 10−3 Scm−1, 59.62% at 45 °C and water loss at 9 min immersion found 38%, respectively. Electromechanical studies of the PsGZ-Pt membrane (size 30 mm length, 10 mm width, 0.07 mm thickness) showed an actuation force of 0.3253 mN and a displacement of roughly 22.8 mm at ± 3 VDC. These findings highlighted the PsGZ-Pt membrane’s potential as a low-cost alternative to expensive commercial IPMC actuators based on polymers. These results presented a straightforward, low-cost approach for synthesizing PsGZ-Pt utilizing conventional technologies. The PsGZ-Pt membrane shows promise for generating low-cost, high-performance actuation materials with a wide range of industrial applications.

Active Fréedericksz Transition in Active Nematic Droplets

Active nematic liquid crystals have the remarkable ability to spontaneously deform and flow in the absence of any external driving force. While living materials with orientational order, such as the mitotic spindle, can self-assemble in quiescent active phases, reconstituted active systems often display chaotic, periodic, or circulating flows under confinement. Quiescent active nematics are, therefore, quite rare, despite the prediction from active hydrodynamic theory that confinement between two parallel plates can suppress flows. This spontaneous flow transition—named the active Fréedericksz transition by analogy with the conventional Fréedericksz transition in passive nematic liquid crystals under a magnetic field—has been a cornerstone of the field of active matter. Here, we report experimental evidence that confinement in spherical droplets can stabilize the otherwise chaotic dynamics of a 3D extensile active nematics, giving rise to a quiescent—yet still out-of-equilibrium—nematic liquid crystal. The active nematics spontaneously flow when confined in larger droplets. The composite nature of our model system composed of extensile bundles of microtubules and molecular motors dispersed in a passive colloidal liquid crystal allows us to demonstrate how the interplay of activity, nematic elasticity, and confinement impacts the spontaneous flow transition. The critical diameter increases when motor concentration decreases or nematic elasticity increases. Experiments and simulations also demonstrate that the critical confinement depends on the confining geometry, with the critical diameter in droplets being larger than the critical width in channels. Biochemical assays reveal that neither confinement nor nematic elasticity impacts the energy-consumption rate, confirming that the quiescent active phase is the stable out-of-equilibrium phase predicted theoretically. Further experiments in dense arrays of monodisperse droplets show that fluctuations in the droplet composition can smooth the flow transition close to the critical diameter. In conclusion, our work provides experimental validation of the active Fréedericksz transition in 3D active nematics, with potential applications in human health, ecology, and soft robotics. Published by the American Physical Society 2024

Design and Experimental Test of Rope-Driven Force Sensing Flexible Gripper.

Robotic grasping is a common operation scenario in industry and agriculture, in which the force sensing function is a significant factor to achieve reliable grasping. Existing force sensing methods of flexible grippers require intelligent materials or force sensors embedded in the flexible gripper, which causes such problems of higher manufacturing requirements and contact surface properties changing. In this paper, a novel rope-driven force sensing flexible gripper is designed based on the fin-shaped gripper structure, which can realize the grasping sensing functions of contact nodes and contact forces without the need for force sensors. Firstly, the rope-driven force sensing flexible gripper is designed, including the driving unit, the transmission part, the gripper unit, and the force sensing unit. The force sensing unit and the gripper unit are connected by rope, and the prototype of the rope-driven force sensing flexible gripper is completed. Secondly, a force sensing algorithm and control system based on finite element method and grasping geometric relationship are designed to realize the rope-driven force sensing flexible gripper grasping control and sensor data acquisition and processing. Finally, the experimental system of the rope-driven force sensing flexible gripper is built, and the grasping experimental tests of objects with different diameters and different contact nodes are carried out to verify the force sensing function of the rope-driven force sensing flexible gripper. The force sensing flexible gripper designed in this paper can provide a new idea for the design and force sensing method of intelligent robotic grasping system in robotic teaching, scientific research, and industrial applications.

Integrated Deep Learning Architectures for Perception, Control, and Decision-making in Robotics: a Framework for Sensing, Cognition, and Transparent Decision-making

The increasing complexity of robotic applications demands innovative approaches for addressing problems that lack analytical solutions, with deep learning (DL) emerging as a key tool for enabling robots to learn and adapt in dynamic environments. This survey reviews existing DL techniques in robotics, categorizing the major challenges and exploring successful solutions that leverage DL for perception, control, and decision-making. We discuss the use of modular versus end-to-end DL architectures, providing guidelines for selecting appropriate model structures and training strategies. The review also examines advancements in neuro-robotics systems (NRS), where the convergence of neuroscience and robotics is driving the development of robots with embodied intelligence, enabling more natural human-robot interactions. Recent progress in neural mechanisms for perception, cognition, learning, and control is highlighted, offering insights into creating future neuro-robots. Furthermore, we address the challenge of uncertainty estimation in neural networks, crucial for reliable robotic decision-making, and evaluate existing frameworks such as Bayesian belief networks and Monte Carlo sampling that improve uncertainty modeling without requiring architectural changes. Lastly, we explore efforts to enhance transparency in robotic systems through integrated reasoning and learning methods, focusing on architectures that combine logical reasoning with deep learning to provide explainable decision-making. This survey aims to offer a structured overview of current research and guide future developments in neuro-robotics.

Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application

The mining industry is experiencing a transformative shift with the integration of automation, particularly through autonomous haul truck systems, and further advancements are anticipated with the application of swarm robotics. This study evaluates the performance of four swarm robot models, namely baseline, ant, firefly, and honeybee, in optimizing key mining operations such as ore detection, extraction, and transportation. Simulations replicating real-world mining environments were conducted to assess improvements in operational efficiency, scalability, reliability, selectivity, and energy consumption. The results demonstrate that these models can significantly enhance the precision and productivity of mining activities, especially in complex and dynamic settings. A case study of the Pilbara iron ore mine in Australia is presented to illustrate the practical applicability of these models in an actual mining context. The study also highlights specific enhancements in each model, including role specialization in the ant model, advanced communication in the firefly model, and improved localization combined with hybrid control in the honeybee model. While the honeybee model showed superior performance in high-precision tasks, its reliability was limited under high-error conditions, and it faced a computational resources bottleneck in large-scale operations, highlighting the need for further development. By evaluating these models against performance criteria, the study identifies the most suitable swarm models for various mining conditions, offering insights into achieving more sustainable, scalable, and efficient mining operations.

Dynamic analysis of six-bar tensegrity-based robot

Abstract The tensegrity-based robot is a hot research topic in the research field of robotics. The dynamics of the robot, describing the specific motion patterns under the influence of forces and torques, play an important role in the application of robotics. In addition, the dynamics should be considered when revealing the internal mechanisms of robots and predicting their dynamic behaviors. In this work, a dynamic model for a rolling six-bar tensegrity-based robot is established. In terms of kinematic representation, quaternions are chosen as the tool for attitude description, simplifying the derivation process of the complex three-dimensional rotational kinematics calculations. Quaternions play a crucial role in mathematical processing for robot attitude control and path planning due to their effective representation of three-dimensional space rotations. The classic Newton-Euler dynamic framework was adopted to conduct in-depth and detailed studies on the dynamic characteristics of the robot under various force conditions and motion states, with a particular focus on rolling dynamic analysis. Special attention was paid to the dynamic response mechanisms of each component of the robot under the combined action of internal and external forces and torques.

Wet-Spinning Carbon Nanotube/Shape Memory Polymer Composite Fibers with High Actuation Stress and Predesigned Shape Change.

Actuators based on shape memory polymers and composites incorporating nanomaterial additives have been extensively studied; achieving both high output stress and precise shape change by low-cost, scalable methods is a long-term-desired yet challenging task. Here, conventional polymers (polyurea) and carbon nanotube (CNT) fillers are combined to fabricate reinforced composite fibers with exceptional actuation performance, by a wet-spinning method amenable for continuous production. It is found that a thermal-induced shrinkage step could obtain densified strong fibers, and the presence of CNTs effectively promotes the tensile orientation of polymer molecular chains, leading to much improved mechanical properties. Consequently, the CNT/ polyurea composite fibers exhibit stresses as high as 33MPa within 0.36 s during thermal actuation, and stresses up to 22MPa upon electrical stimulation enabled by the built-in conductive CNT networks. Utilizing the flexible thin fibers, various shape change behavior are also demonstrated including the conversion between different structures/curvatures, and recovery of predefined simple patterns. This high-performance composite fibers, capable of both thermal and electrical actuation and produced by low-cost materials and fabrication process, may find many potential applications in wearable devices, robotics, and biomedical areas.

SonoPrint: Acoustically Assisted Volumetric 3D Printing for Composites.

Advances in additive manufacturing in composites have transformed aerospace, medical devices, tissue engineering, and electronics. A key aspect of enhancing properties of 3D-printed objects involves fine-tuning the material by embedding and orienting reinforcement within the structure. Existing methods for orienting these reinforcements are limited by pattern types, alignment, and particle characteristics. Acoustics offers a versatile method to control the particles independent of their size, geometry, and charge, enabling intricate pattern formations. However, integrating acoustics into 3D printing has been challenging due to the scattering of the acoustic field between polymerized layers and unpolymerized resin, resulting in unwanted patterns. To address this challenge, SonoPrint, an innovative acoustically assisted volumetric 3D printer is developed that enables simultaneous reinforcement patterning and printing of the entire structure. SonoPrint generates mechanically tunable composite geometries by embedding reinforcement particles, such as microscopic glass, metal, and polystyrene, within the fabricated structure. This printer employs a standing wave field to create targeted particle motifs-including parallel lines, radial lines, circles, rhombuses, hexagons, and polygons-directly in the photosensitive resin, completing the print in just a few minutes. SonoPrint enhances structural properties and promises to advance volumetric printing, unlocking applications in tissue engineering, biohybrid robots, and composite fabrication.

Modeling and simulation study of acoustic response for dual-membrane capacitive MEMS microphone

Abstract As the micro-electro-mechanical systems (MEMS) technology matures, MEMS sensors have found widespread applications in mechatronics, robotics, and voice control. The high stability, high integration, and radio frequency interference resistance of MEMS microphones have rapidly led to their adoption in these domains, displacing electret condenser microphones.This article focuses on modeling and analyzing dual-membrane capacitive MEMS microphone, utilizing an lumped equivalent circuit model to calculate the microphone’s acoustic frequency response. Furthermore, the article employs the finite element method (FEM) to conduct a detailed analysis of the resonant frequency of the membrane, as well as to explore the effects of changes in the volume of the front and back chamber on the packaging performance of MEMS microphones. Finally, physical tests were conducted, and the simulation results of the two models showed considerable consistency with the curves obtained from the physical tests, verifying the accuracy of the models.

Customizable metamaterial design for desired strain-dependent Poisson’s ratio using constrained generative inverse design network

Inverse design of metamaterial structures with customized strain-dependent Poisson’s ratio has significant potential across various applications. However, achieving precise control over these mechanical properties presents a challenge due to the complex relationship between geometry and mechanical performance. Here, we present a novel data-driven approach utilizing a constrained generative inverse design network (CGIDN) to address this challenge. The CGIDN uses backpropagation to efficiently navigate the design space and achieve target mechanical properties with high accuracy. Our method starts by generating a comprehensive dataset of Poisson’s ratio-strain curves for various geometries incorporating cuts. These curves are then compressed using principal component analysis (PCA) to reduce dimensionality while preserving essential features. A deep neural network (DNN) is then trained to map input geometric parameters to these principal components, with the architecture optimized using grid search. The CGIDN facilitates the inverse design process by recommending geometric parameters for unit cell designs that match specified target Poisson’s ratio-strain curves. We validated the effectiveness of our approach through Finite Element Analysis (FEA) and experimental verification. The FEA results for the designed unit cells showed high agreement with the target and predicted curves, demonstrating the accuracy of the CGIDN model. Further, tensile tests on specimens confirmed that the inverse-designed structures reproduced the desired mechanical behavior upon scale-up. Our method, which enables efficient and accurate design of metamaterials with tailored mechanical properties, holds promise for applications in wearable devices, soft robotics, and advanced sensor systems

Optical resonant cavities carving pathways in tunable wavelength sensitive visible-NIR organic photodetectors

Enhancing the photodetection capabilities of organic photodetectors (OPDs) is crucial for advancing applications in medical monitoring, optical communications, image sensing, and robotics, where a strong, focused peak response at a specific designed wavelength is essential for improving sensitivity, wavelength selectivity, and resolution in imaging systems. By controlled integration of ZnO layers within PBDBT:BTP-4F-based OPDs to form a Fabry-Pérot optical cavity, we developed a cost-effective approach to fabricating highly sensitive OPDs by utilizing PBDBT:BTP-4F organic bulk heterojunctions, and extended its detection wavelengths into the near-infrared (NIR) range. Our design integrates a single silver (Ag) layer that significantly enhances peak detection at a wavelength of 830 ​nm, resulting in a remarkably narrow full-width at half maximum (FWHM) wavelength of 30 ​nm and yielding a photoresponse ten times greater than that of non-resonant devices. Furthermore, by varying the thickness of the ZnO layer from 77 ​nm to 620 ​nm, we achieve high spectral tunability, allowing fine adjustments of the resonant peak across a spectrum ranging from ultraviolet (UV) and visible to NIR wavelengths. This sensitive photodetector is also well-suited for applications in photoplethysmography (PPG), effectively detecting pulse signals in the NIR spectrum which has significant potential in medical diagnostics. This work advances the integration of cost-effective, wavelength-selective spectroscopic visible-NIR OPDs, paving the way for the next generation of sensitive photodetectors.

Deformation and enclosed volume change of axisymmetric non-linear dielectric elastomer membrane pump: a theoretical study

Abstract This work investigates the change in the enclosed volume of an axisymmetric non-linear hyper-elastic membrane pump subjected to dielectric actuation. The equilibrium equations in cylindrical coordinates, coupled with non-linear constitutive relations, are solved numerically to obtain the deformed membrane geometry under pressure loading (using Variational Calculus). The effect of dielectric actuation is then incorporated by considering the change in material properties and deformation induced by the applied electric field. The deformed geometry under dielectric actuation is determined, and the change in enclosed volume is calculated by comparing the deformed states with and without the applied electric field. The proposed approach enables quantifying the volumetric change in non-linear hyper-elastic membrane pumps due to dielectric actuation for potential applications in soft robotics, adaptive optics, and microfluidics. By non-dimensionalizing variables, key dimensionless parameters governing the problem are identified for broader applicability. Furthermore, this methodology can estimate volume changes for different electric actuations when a specific material is chosen with experimentally derived parameter trends, facilitating material selection or synthesis to meet targeted volumetric requirements.