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Biologically inspired robots with inherent softness and body compliance increasingly attract attention in the field of robotics. Aimed at solving existing problems with soft robots, regarding actuation technology and biological principles, this paper presents a soft bio-inspired annelid robot driven by dielectric elastomer actuators (DEAs) that can advance on flat rigid surfaces. The DEA, a kind of soft functional actuator, is designed and fabricated to mimic the axial elongation and differential friction of a single annelid body segment. Several (at least three) DEAs are connected together into a movable multi-segment robot. Bristles are attached at the bottom of some DEAs to achieve differential friction for imitating the setae of annelids. The annelid robot is controlled by periodic square waves, propagating from the posterior to the anterior, which imitate the peristaltic waves of annelids. Controlled by these waves, each DEA, one-by-one from tail to head, anchors to the ground by circumferential distention and pushes the front DEAs forward by axial elongation, enabling the robot to advance. Preliminary tests demonstrate that a 3-segment robot can reach an average speed of 5.3 mm s (1.871 body lengths min) on flat rigid surfaces and can functionally mimic the locomotion of annelids. Compared to the existing robots that imitate terrestrial annelids our annelid robot shows advantages in terms of speed and bionics.

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Dielectric elastomer actuators (DEAs) are known as a type of electric-driven artificial muscle that have shown promising potential in the field of soft robotics. However, the inherent viscoelastic nonlinearity makes the modeling and control of DEAs challenging. In this paper, we propose a new phenomenological modeling approach with the Prandtl-Ishlinskii (P-I) model to characterize the viscoelastic hysteresis nonlinearity of DEAs. Differently from the commonly used physics-based models, the developed phenomenological model, called the modified rate-dependent P-I model (MRPIM), produces behavior similar to that of physics-based models but without necessarily considering physical insight into the modeling problem. In this way, the developed MRPIM can characterize the asymmetric and rate-dependent viscoelastic hysteresis with a relative simple mathematical format using only the experimental data. To validate the development, experimental tests were conducted with seven different frequencies; four were selected to identify the model parameters and the rest of the data were used to further verify the model. Comparisons between the model prediction and experimental data demonstrate that the MRPIM can precisely describe the viscoelastic hysteresis effect of DEAs with a maximum prediction error of 9.03% and root-mean-square prediction error of 4.50%.

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