EuMoBot: replicating euglenoid movement in a soft robot

Abstract

Swimming is employed as a form of locomotion by many organisms in nature across a wide range of scales. Varied strategies of shape change are employed to achieve fluidic propulsion at different scales due to changes in hydrodynamics. In the case of microorganisms, the small mass, low Reynolds number and dominance of viscous forces in the medium, requires a change in shape that is non-invariant under time reversal to achieve movement. The Euglena family of unicellular flagellates evolved a characteristic type of locomotion called euglenoid movement to overcome this challenge, wherein the body undergoes a giant change in shape. It is believed that these large deformations enable the organism to move through viscous fluids and tiny spaces. The ability to drastically change the shape of the body is particularly attractive in robots designed to move through constrained spaces and cluttered environments such as through the human body for invasive medical procedures or through collapsed rubble in search of survivors. Inspired by the euglenoids, we present the design of EuMoBot, a multi-segment soft robot that replicates large body deformations to achieve locomotion. Two robots have been fabricated at different sizes operating with a constant internal volume, which exploit hyperelasticity of fluid-filled elastomeric chambers to replicate the motion of euglenoids. The smaller robot moves at a speed of Inline Formula body lengths per cycle (20 mm min−1 or 2.2 cycles min−1) while the larger one attains a speed of Inline Formula body lengths per cycle (4.5 mm min−1 or 0.4 cycles min−1). We show the potential for biomimetic soft robots employing shape change to both replicate biological motion and act as a tool for studying it. In addition, we present a quantitative method based on elliptic Fourier descriptors to characterize and compare the shape of the robot with that of its biological counterpart. Our results show a similarity in shape of 85% and indicate that this method can be applied to understand the evolution of shape in other nonlinear, dynamic soft robots where a model for the shape does not exist.

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