A Robotic Testing Platform for Pipelined Discovery of Resilient Soft Actuators
Authors: Ang Li, Alexander Yin, Alexander White, Sahib Sandhu, Matthew Francoeur, Victor Jimenez-Santiago, Van Remenar, Codrin Tugui, Mihai Duduta
Organizations: Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, M5S 3G8 Canada · Institute of Materials Science, University of Connecticut, Storrs, CT, 06269 U.S.A. · School of Mechanical, Aerospace, and Manufacturing Engineering, University of Connecticut, Storrs, CT, 06269 U.S.A. · Material Science and Engineering, University of Connecticut, Storrs, CT, 06269 U.S.A. · Inorganic Polymers Department, Petru Poni Institute of Macromolecular Chemistry, Iasi, 700487 Romania
Short lifetime under high electrical fields hinders the widespread robotic application of linear dielectric elastomer actuators (DEAs). Systematic scanning is difficult due to time-consuming per-sample testing and the high-dimensional parameter space affecting performance. To address this, we propose an optimization pipeline enabled by a novel testing robot capable of scanning DEA lifetime. The robot integrates electro-mechanical property measurement, programmable voltage input, and multi-channel testing capacity. Using it, we scanned the lifetime of Elastosil-based linear actuators across parameters including input voltage magnitude, frequency, electrode material concentration, and electrical connection filler. The optimal parameter combinations improved operational lifetime under boundary operating conditions by up to 100% and were subsequently scaled up to achieve higher force and displacement output. The final product demonstrated resilience on a modular, scalable quadruped walking robot with payload carrying capacity (>100% of its untethered body weight, and >700% of combined actuator weight). This work is the first to introduce a self-driving lab approach into robotic actuator design.
Figures & tables
Figure 1: Linear DEA optimization pipeline. Batches of linear DEA samples are fabricated and tested using the DEA testing robot. lifetime is scanned across the parameter space defined by voltage, frequency, and material choices. The optimal parameter combination is then used to scale up the actuator in size and layer number, achieving higher displacement and force output. Finally, the optimized actuators are integrated into a quadruped walking robot.
Figure 2: The linear DEA testing robot. A, the testing robot. B, the block diagram of the system architecture. C, the top-down and side views of the testing robot. D, the photos of the top-down and side views of the testing robot.
Figure 3: Parameter optimization for optimal lifetime and mechanical output. A, layered optimization structure. B, Optimization result comparison. Lifetime and average displacement at 1Hz ( 40,V/μm ) and 50Hz ( 45,V/μm ) of best, baseline (default) and worst material combinations are presented. C, 3D model of the scaled up DEA with reinforcement ribbons. D, comparison of displacement under the same electric field for scaled-up DEAs with and without reinforcement ribbons. E, displacement and force degradation of scaled-up DEAs at 1Hz, above 40,V/μm , for 10 hours of continuous actuation.
Figure 4: The Modular quadruped walking robot. A, Robot design and assembly. Base locomotion units serve as the building blocks; two units form a double-unit module, and two double-units assemble into a quadruped robot. B, Gait mechanism of a single locomotion unit. C, Frequency response of walking speed for 1-, 2-, and 4-unit robot configurations. D, Long-term speed stability of a base locomotion unit. E, Payload-dependent walking speed of the tethered quadruped robot. F, Quadruped robot carrying onboard power electronics designed for untethered operation.
Figure 5: The vision of a generalizable device-level SDL for DEA design optimization . A combinatorial fabrication robot (under development) produces device samples, which are transferred by a dexterous handling robot to the testing platform. The high-throughput testing robot conducts comprehensive lifetime measurements, while an FPGA-based node manages data collection and uploads data to a database. Data-driven algorithms are used to generate new fabrication parameters for subsequent iterations. The process converges toward an optimized material and device design, which is then transferred for robotic application.
Figure S1 : Linear actuator assembly. The multilayer actuator is fabricated using standard procedures well-documented in literature [ 41 , 9 ] . After trimming, the multilayers are rolled and placed between two end caps. Carbon tape provides an adhesive interface for conductors, connecting stranded copper wires and conductive sheets. The wires serve as external electrical leads, while the conductive sheets extend the effective contact area with the DEA. A conductive filler layer is applied between the conductive sheet and the multilayer cross section; this layer is critical to the device’s cycle-life performance and is a key focus of material selection in this work. The actuator expands linearly along its length when an electric field is applied across its terminals.
Figure S2 : Data acquisition circuit for the testing robot. (Left) the photo of the circuit board. (Right) The circuit topology.
Figure S3 : Programmable power supply circuit for the testing robot. (Left) the photo of the circuit board. (Right) The circuit topology (cooling fans are not included).
Figure S4 : Testing robot architecture and workflow. (A) System architecture from user interface to hardware. (B) Data collection sequence for full electromechanical measurements. The robot periodically switches measurement modes to capture complete datasets; however, most data in this work were collected using simplified protocols measuring either force or displacement. (C) Workflow of Arduino 1 (top-layer control and DAQ board). Motor actions are shown in blue, and Arduino 2 (high-voltage board) actions in green.
Figure S5 : Displacement performance of the scaled-up actuator. (A) Comparison of DEA actuators with and without ribbon reinforcement at 42 V/μm . No significant difference is observed between the two configurations, both achieving about 2 mm displacement (8.7% axial strain). (B) Displacement versus frequency response of the scaled-up actuator at 45 V/μm , obtained using a sweep signal ranging from 1 Hz to 100 Hz over 100 seconds.
Figure S6 : Circuit diagram for untethered quadruped robot power electronics. The top layer of the board consists of MCU, battery voltage converter, and high voltage MOSFETs for discharge. The bottom layer of the board consists of two high voltage converters (HVM SMHV0520) and 3 high voltage opto-couplers (HVM OR-100).
Figure S7 : Capacitance–frequency sweep measurements of device samples under different electric fields and actuation frequencies. Measurements were performed using Analog Discovery 3 modules mounted on the testing robot. The left plots show capacitance before testing, and the right plots show capacitance after testing. Higher actuation voltages lead to greater capacitance degradation, while the influence of actuation frequency on degradation is less pronounced.
Dielectric elastomer actuators (DEAs) have garnered extensive attention especially in soft robotic applications over the past few decades owing to the advantages of lightweight, large strain, fast response and high energy density. However, because the DEAs suffer from nonlinear elasticity, inherent viscoelastic creep, hysteresis and vibrational dynamics, the modeling, control and self-sensing of DEAs are challenging, thereby hindering the practical applications of DEAs. In order to address these challenges, numerous studies have been conducted. In this review, various physics-based modeling methods and phenomenological modeling methods for predicting the electromechanical response of DEAs are presented and discussed. Different control methods for DEAs are reviewed, which are classified into open-loop feedforward control, feedback control, feedforward-feedback control and adaptive feedforward control. Physics-based self-sensing methods and data-driven self-sensing methods for reconstructing the DEA displacement without the need for additional sensors are discussed. Finally, the existing problems and new opportunities for the further studies are summarized.
Y. Zhao, G. Meng
School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China · State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Machines designed for operation in Space, as well as other extreme environments, need to be both resilient and adaptable when mission parameters change. Soft robots offer advantages in adaptability, but most lack resilience to the pressure and temperature extremes found as close as the Stratosphere. Dielectric elastomer actuators overcome some of those limitations when built as solid state compliant capacitors capable of converting electrical energy into mechanical work, but the elastomer resilience limits the device's operating window. Here we present a crosslinking mechanism for silicone elastomers under ultraviolet light using trimethyl(methylcyclopentadienyl)platinum(IV) as a catalyst to react hydrosilane to vinyl groups. The formation of carbon-carbon bonds enables fast processing under UV light and exceptional electro-mechanical performance in dielectric elastomer actuators. The material resilience advantage is demonstrated in controlled experiments at -40° and 120° C, as well as near vacuum, in comparison with state-of-the-art acrylic and silicone chemistries. Fully autonomous systems controlling grippers made with the novel silicone were integrated into payloads for high altitude balloon testing. Two stratospheric balloon missions were carried out and demonstrated DEAs as a viable soft robotic technology under space-like conditions (as high as 23.6 km elevation, at <0.05 atm and -55° C). The combinations of chemical building blocks and catalyst can be further expanded to address other challenges for silicones, including adhesion and additive manufacturing.
Codrin Tugui, Tirth Thakar, Anatol Gogoj +5
University of Connecticut, School of Mechanical, Aerospace, and Manufacturing Engineering, Storrs, Connecticut USA · Institute of Macromolecular Chemistry Petru Poni; Iași, Romania · University of Toronto, Department of Mechanical and Industrial Engineering, Toronto, Ontario, Canada
Series and parallel elastic actuators offer complementary but mutually exclusive advantages, yet no existing actuator enables real-time transition between these topologies during operation. This paper presents a novel actuator design called the Dual-Topology Elastic Actuator (DTEA), which enables dynamic switching between SEA and PEA topologies during operation. A proof-of-concept prototype of the DTEA is developed to demonstrate the feasibility of the topology-switching mechanism. Experiments are conducted to evaluate the robustness and timing of the switching mechanism under operational conditions. The actuator successfully performed 324 topology-switching cycles under load without damage, demonstrating the robustness of the mechanism. The measured switching time between SEA and PEA modes is under 33.33 ms. Additional experiments are conducted to characterize the static stiffness and disturbance rejection performance in both SEA and PEA modes. Static stiffness tests show that the PEA mode is 1.53x stiffer than the SEA mode, with KSEA = 5.57 +/- 0.02 Nm/rad and KPEA = 8.54 +/- 0.02 Nm/rad. Disturbance rejection experiments show that the mean peak deflection in SEA mode is 2.26x larger than in PEA mode (5.2 deg vs. 2.3 deg), while the mean settling time is 3.45x longer (1380 ms vs. 400 ms). The observed behaviors are consistent with the known characteristics of conventional SEA and PEA actuators, validating the functionality of both modes in the DTEA actuator.
Vishal Ramesh, Aman Singh, Shishir Kolathaya
Department of Cyber-Physical Systems (CPS), Indian Institute of Science (IISc), Bengaluru, India