Evaluation of Brain Models to Control a Robotic Origami Arm Using Holographic Neural Networks

Abstract

In robotics, one of the most difficult task is to perform a precisely and fast movement of a robotic arm. For paper-folding robots, it is still extremely difficult to execute the required manipulations of the paper mainly because the difficulties in modeling and control of the paper. In this paper two control models are proposed to solve this problem. One of the best approaches comes from Neuroscience, where using a human’s brain inspired control system known as Cerebellar control model (CCM), precisely and fast movements of a robotic arm can be performed. In the CCM a Feedback controller motor command is used as a target signal to train an Artificial Neural Network (NN), and use the output of the NN as a Feed-forward signal. In this paper two training methods were evaluated in order to improve the behavior in CCM: the traditional Back propagation and a Holographic method.

A Product of the Instructional Design Process Developed According to the Seels and Glasgow Model: Interactive Hologram-Supported Material Set

“…When the relevant literature about the use of three-dimensional hologram technology in education was examined, it was observed that this technology is used more frequently in medical science and engineering (Golden, 2017;Hackett, 2013;Hackett, 2018;Freeman, 2010;Kalansooriya et al, 2015;Musion, 2014;Khan et al, 2013, Romero et al, 2015Sudeep, 2013;Vanden Bosch et al, 2005), while no studies were encountered in the area of preschool science education. However, in their study with science teacher candidates, Okulu and Ünver (2016) mentioned that the education given with this technology would favo urably affect spatial thinking ability and depth perception.…”