Learning Soft Robot and Soft Actuator Dynamics using Deep Neural Network

Authors

  • Hari Prakash Thanabalan

Abstract

Inspired by living organisms and being the forefront of robotics evolution, the research in soft robotics has been growing exponentially. Due to the flexibility of these robots that is made from soft materials such as silicone or even a fabric allows them to manoeuvre on secluded environments through crevice openings which bring many advantages comparing to the rigid-component robots which proves much more delicate interaction with humans and environments. In this paper, modelling of the soft robot using finite element modelling will be discussed in conjunction with deep neural network for the bending and control of the end effector.

Downloads

Download data is not yet available.

Downloads

Published

2021-04-30

How to Cite

Thanabalan, H. P. (2021). Learning Soft Robot and Soft Actuator Dynamics using Deep Neural Network. International Journal of Electrical Engineering and Applied Sciences (IJEEAS), 4(1). Retrieved from https://ijeeas.utem.edu.my/ijeeas/article/view/6060

Issue

Vol. 4 No. 1 (2021): Electrical Engineering and Applied Sciences

Section

Articles

License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).