Low Voltage Electrohydraulic Actuators for Untethered Robotics

Rigid robots can be precise in repetitive tasks but struggle in unstructured environments. Nature's versatility in such environments inspires researchers to develop biomimetic robots that incorporate compliant and contracting artificial muscles. Among the recently proposed artificial muscle technologies, electrohydraulic actuators are promising since they offer comparable performance to mammalian muscles in terms of speed and power density. However, they require high driving voltages and have safety concerns due to exposed electrodes. These high voltages lead to either bulky or inefficient driving electronics that make untethered, high-degree-of-freedom bio-inspired robots difficult to realize. Here, we present low voltage electrohydraulic actuators (LEAs) that match mammalian skeletal muscles in average power density (50.5 W/kg) and peak strain rate (971 percent/s) at a driving voltage of just 1100 V. This driving voltage is approx. 5 - 7 times lower compared to other electrohydraulic actuators using paraelectric dielectrics. Furthermore, LEAs are safe to touch, waterproof, and self-clearing, which makes them easy to implement in wearables and robotics. We characterize, model, and physically validate key performance metrics of the actuator and compare its performance to state-of-the-art electrohydraulic designs. Finally, we demonstrate the utility of our actuators on two muscle-based electrohydraulic robots: an untethered soft robotic swimmer and a robotic gripper. We foresee that LEAs can become a key building block for future highly-biomimetic untethered robots and wearables with many independent artificial muscles such as biomimetic hands, faces, or exoskeletons.

Computational Design of Active Kinesthetic Garments

Garments with the ability to provide kinesthetic force-feedback on-demand can augment human capabilities in a non-obtrusive way, enabling numerous applications in VR haptics, motion assistance, and robotic control. However, designing such garments is a complex, and often manual task, particularly when the goal is to resist multiple motions with a single design. In this work, we propose a computational pipeline for designing connecting structures between active components - one of the central challenges in this context. We focus on electrostatic (ES) clutches that are compliant in their passive state while strongly resisting elongation when activated. Our method automatically computes optimized connecting structures that efficiently resist a range of pre-defined body motions on demand. We propose a novel dual-objective optimization approach to simultaneously maximize the resistance to motion when clutches are active, while minimizing resistance when inactive. We demonstrate our method on a set of problems involving different body sites and a range of motions. We further fabricate and evaluate a subset of our automatically created designs against manually created baselines using mechanical testing and in a VR pointing study.