alexa Brain-Swarm Control Interfaces: from the Control of One to the Control of Many
ISSN: 2168-9695

Advances in Robotics & Automation
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Brain-Swarm Control Interfaces: from the Control of One to the Control of Many

Panagiotis Artemiadis*

Director of the ASU Human-Oriented Robotics and Control (HORC) Lab, School for Engineering of Matter, Transport and Energy, Arizona State University, USA

*Corresponding Author:
Artemiadis P
Director of the ASU Human-Oriented Robotics and Control (HORC) Lab
School for Engineering of Matter
Transport and Energy, Arizona State University, USA
Tel: +480965-4182
Fax: +480727-9321
E-mail: [email protected]

Received Date: January 06, 2017; Accepted Date: January 08, 2017; Published Date: January 16, 2017

Citation: Artemiadis P (2017) Brain-Swarm Control Interfaces: The Transition from Controlling One Robot to a Swarm of Robots. Adv Robot Autom 6: e127. doi: 10.4172/2168-9695.1000e127

Copyright: © 2017 Artemiadis P. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



Brain-Machine Interfaces (BMIs) have been one of the most influential and disruptive science fields of the past decades. Prosthetic or remotely operated robotic devices being controlled by brain signals has transitioned from science fiction to reality. Advances in the recording electrodes technology and the machine learning and signal decoding algorithms were critical in the realization of those systems. The second decade of the 21st century brings new challenges found in both frontiers; first, advancements in neuroscience are sought via high-resolution mapping of the brain for better understanding of its function and decision making processes. On the robotics frontiers, the challenge of the human controlling many robots simultaneously is of utmost importance for applications spanning from industrial and entertainment, to disaster response and military. As the swarming paradigm, deriving inspiration from the behaviour of natural swarms such as bird flocks and fish schools, offers myriad advantages to a team of robots, the way humans interact and control a robotic swarm creates new avenues of research. This article summarizes recent developments and novel methods for brain-swarm interfaces, and poses challenges for the future researchers.


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