Exploiting the Dynamics of Tensegrities Via Morphological Computation

Tensegrity robots are a class of soft robots which are comprised of rigid struts connected by springs under tension in such a way as to maintain a flexible resting form. These robots have dynamic and complex physical properties which make it impossible to predict how an actuation will affect the system. We are attempting to utilize, rather than mitigate, this physical dynamism by using morphological computation, which treats the physical system as implicitly performing computations. Specifically, we will implement a simple spiking neuron on each strut and treat the system as a whole as a spiking neural network, with communication between neurons implicit in the vibrational activity of the strut and looking for emergent behavior. Ultimately, we hope that the physical system can be used to produce a robust central pattern generator to generate an effective forward gait for the robot.