Evaluation of Grasshopper Inspired Spring Actuation Model for Tensegrity Robot Locomotion

Tensegrities are popular structures for soft robots due to their robust properties but are also difficult to move in meaningful ways. Looking at movement methods in grasshoppers, which are able to move many times their body length in short intervals, may lead to discovering more effective movement patterns for tensegrity structures. Much of the grasshopper’s effective locomotion is due to the the spring-like structures in its hind legs which store and release energy needed for movement. Tensegrities also have spring structures which can be contracted to produce movement. Spring stiffness varies in grasshoppers between species and stages of development. By Hooke’s law of spring dynamics, F = kx, altering the spring stiffness should increase the force production linearly. We explore the effects of changing spring stiffness on distance traveled in a tensegrity robot in simulation within Open Dynamics Environment. Six of the Twenty Four springs within the tensegrity robot were chosen to be actuated. The spring stiffnesses of these six springs were either changed uniformly or individually to determine if novel tensegrity movement would be produced. Spring stiffness values were optimized using the Covariance Matrix Adaptation Evolution Strategy. Unlike grasshoppers which have increased jump performance with greater spring stiffness, the resulting displacement values of the tensegrity did not follow a linear trend with changes in spring stiffnesses. They did not converge as expected by Hooke’s law to the greatest possible value. This suggests that altering the spring stiffnesses in tensegrities could lead to more diverse patterns of locomotion which may also not follow a linear trend with increasing spring stiffness.