This paper shows and evaluates a novel approach to integrate a non-invasive Brain-Computer Interface (BCI) with the Robot Operating System (ROS) to mentally drive a telepresence robot. Controlling a mobile device by using human brain signals might improve the quality of life of people suffering from severe physical disabilities or elderly people who cannot move anymore. Thus, the BCI user can actively interact with relatives and friends located in different rooms thanks to a video streaming connection to the robot. To facilitate the control of the robot via BCI, we explore new ROS-based algorithms for navigation and obstacle avoidance in order to make the system safer and more reliable. In this regard, the robot exploits two maps of the environment, one for localization and one for navigation, and both are used as additional visual feedback for the BCI user to control the robot position. Experimental results show a decrease of the number of commands needed to complete the navigation task, suggesting a reduction user’s cognitive workload. The novelty of this work is to provide a first evidence of an integration between BCI and ROS that can simplify and foster the development of software for BCI driven robotics devices.

Brain-Computer Interface meets ROS: A robotic approach to mentally drive telepresence robots

BERALDO, GLORIA
;
Morris Antonello;CIMOLATO, ANDREA;Emanuele Menegatti;Luca Tonin
2018

Abstract

This paper shows and evaluates a novel approach to integrate a non-invasive Brain-Computer Interface (BCI) with the Robot Operating System (ROS) to mentally drive a telepresence robot. Controlling a mobile device by using human brain signals might improve the quality of life of people suffering from severe physical disabilities or elderly people who cannot move anymore. Thus, the BCI user can actively interact with relatives and friends located in different rooms thanks to a video streaming connection to the robot. To facilitate the control of the robot via BCI, we explore new ROS-based algorithms for navigation and obstacle avoidance in order to make the system safer and more reliable. In this regard, the robot exploits two maps of the environment, one for localization and one for navigation, and both are used as additional visual feedback for the BCI user to control the robot position. Experimental results show a decrease of the number of commands needed to complete the navigation task, suggesting a reduction user’s cognitive workload. The novelty of this work is to provide a first evidence of an integration between BCI and ROS that can simplify and foster the development of software for BCI driven robotics devices.
2018
IEEE International Conference on Robotics and Automation (2018)
978-1-5386-3081-5
File in questo prodotto:
File Dimensione Formato  
2018_icra_beraldo.pdf

non disponibili

Tipologia: Published (publisher's version)
Licenza: Accesso privato - non pubblico
Dimensione 1.23 MB
Formato Adobe PDF
1.23 MB Adobe PDF Visualizza/Apri   Richiedi una copia
1712.01772.pdf

accesso aperto

Tipologia: Preprint (submitted version)
Licenza: Accesso libero
Dimensione 641.48 kB
Formato Adobe PDF
641.48 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3266529
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 31
  • ???jsp.display-item.citation.isi??? 11
social impact