Hongying Zhang

A soft compressive sensor using dielectric elastomers

This paper proposes a methodology to design, analyze and fabricate a soft compressive sensor, made of dielectric elastomers that are able to recover from large strain. Each module of the compressive sensor is modeled as a capacitor, comprising a DE membrane sandwiched between two compliant electrodes. When the sensor modules aligned in an array were subject to a compressive load, the induced deformation on the corresponding module resulted in capacitance increase. By detecting the capacitance signal, not only the position but also the magnitude of the compressive load were obtained. We built an analytical model to simulate the mechanical–electrical responses of two common soft sensor structures, namely with and without an embedded air chamber. The simulation results showed that the air embedded prototype improved the sensitivity of the sensor significantly, which was consistent with the experimental results, where the sensitivity is enhanced from 0.05 N−1 to 0.91 N−1. Furthermore, the effect of the air chamber dimension on the sensitivity is also discussed theoretically and experimentally. It concluded that the detection range increased with the air chamber height over length ratio.

Networked soft actuators with large deformations

Soft actuators play an important role in producing motions in soft robots, and dielectric elastomers have shown great promise because of their considerable voltage-induced deformation. In particular, air-filled dielectric elastomer actuators have been well studied, where the air inside provides prestretches to improve the actuation range. This paper proposes a network of inflated dielectric elastomer actuators, interconnected via a chamber, with the advantages to be highly deformable and continuously controllable. Theoretical analyses show that the networked design is able to largely postpone the occurrence of material failures of the actuators, resulting in a large and continuous actuation range for their control. We further carried out experiments for validation, and the results were largely in line with the theoretical predictions. These findings essentially provide insight into developing networked soft actuators, for achieving large actuation capability.