Structured Computational Polymers for a soft robot: Actuation and cognition

Abstract

Structured Computational Polymers (SCP) is a concept of layered class of active material that can sense its environment and, due to its cognitive capabilities, react “intelligently” to those changes. In such a material, we envision semiconducting polymer based sensing, actuation, and information processing for on-board decision making to be combined into one active material. This paper describes incremental steps taken towards developing such a multifunctional active material, concentrating on distributed forms of actuation and cognition, with an intermediate goal of utilizing SCP as a “skin” of a soft robot - a robot, made of flexible materials, which is not bounded by its rigid structure and can adjust to its changing environment - with its sensing, cognition, and actuation embedded in the shape. We demonstrate, via experiment and rudimentary simulation, the feasibility of utilizing water hammer as a form of directed, distributed actuation. We also show that distributed form of cognition can be realized via a novel concept termed Synthetic Neural Network (SNN), which is a type of organic neuromorphic architecture modeled after Artificial Neural Network. SNN, based on a single-transistor-single-memristor-per-input for an individual neuron, can approximate the sigmoidal activation function with an accuracy of about 3%. A simulation of the SNN is shown to accurately predict the directionality of water hammer propulsion with an accuracy of 7.2 percent.