A Robotic Testing Platform for Pipelined Discovery of Resilient Soft Actuators

Abstract

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 (12)

• 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 ($40,V/\mu m$) and 50Hz ($45,V/\mu m$) 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 $40,V/\mu m$, 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.