ICRA 2011 Paper Abstract

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Paper ThA208.3

Nawrocki, Robert (University of Denver), Yang, Xiaoting (University of Denver), Shaheen, Sean (University of Denver), Voyles, Richard (University of Denver)

Structured Computational Polymers for a Soft Robot: Actuation and Cognition

Scheduled for presentation during the Regular Sessions "Soft Material Robotics" (ThA208), Thursday, May 12, 2011, 10:35−10:50, Room 5C

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on May 23, 2019

Keywords Soft Material Robotics, Neural and Fuzzy Control, Motion Control of Manipulators

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.

 

 

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