Biologically Inspired SNN for Robot Control-Reference-Cited by-同舟云学术

Biologically Inspired SNN for Robot Control

Published:2023-06-16 Issue: Volume: Page:289-318
ISSN:2327-3453
Container-title:Advances in Systems Analysis, Software Engineering, and High Performance Computing
language:
Short-container-title:

Author:

Ganeshkumar S.¹,Maniraj J.²,Gokul S.¹,Ramaswamy Krishnaraj³

Affiliation:

1. Sri Eshwar College of Engineering, Coimbatore, India

2. KalaignarKarunanidhi Institute of Technology, Coimbatore, India

3. College of Engineering and Technology, Ethiopia

Abstract

In recent years, there has been a trend towards more sophisticated robot control. This has been driven by advances in artificial intelligence (AI) and machine learning, which have enabled robots to become more autonomous and effective in completing tasks. One trend is towards using AI for robot control. This involves teaching robots how to carry out tasks by providing them with data and letting them learn from it. This approach can be used for tasks such as object recognition and navigation. Another trend is towards using machine learning for robot control. This involves using algorithms to learn from data and improve the performance of the robot. This approach can be used for tasks such as object recognition and navigation. A third trend is towards using more sophisticated sensors for robot control. This includes using sensors that can detect things such as temperature, humidity, and pressure.

Publisher

IGI Global

Reference64 articles.

1. An ultra-low-power static random-access memory cell using tunneling field effect transistor.;N.Arunkumar;International Journal of Engineering,2020

2. A consecutive hybrid spiking-convolutional (CHSC) neural controller for sequential decision making in robots

3. Bioinspired Hydrogels as Platforms for Life-Science Applications: Challenges and Opportunities

4. Application of switching median filter with L2 norm-based auto-tuning function for removing random valued impulse noise

5. Casanueva-Morato, D., Ayuso-Martinez, A., Dominguez-Morales, J. P., Jimenez-Fernandez, A., & Jimenez-Moreno, G. (2022). Spike-based computational models of bio-inspired memories in the hippocampal CA3 region on SpiNNaker. arXiv preprint arXiv:2205.04782.