Brain-Actuated Humanoid Robot Navigation Control | Electrical Project
Brain-Actuated Humanoid Robot Navigation Control
Abstract
Brain-actuated robotic systems have been proposed as a new
control interface to translate different human intentions into appropriate
motion commands for robotic applications. This study proposes a brain-actuated
humanoid robot navigation system that uses an EEG-BCI.
The experimental procedures consisted of offline training
sessions, online feedback test sessions, and real-time control sessions. During
the offline training sessions, amplitude features from the EEGs were extracted
using band power analysis, and the informative feature components were selected
using the Fisher ratio and the linear discriminant analysis (LDA) distance
metric.
The Intentional Activity Classifier (IAC) and the Motor
Direction Classifier (MDC) were hierarchically structured and trained to build
an asynchronous BCI system.
During the navigation experiments, the subject controlled
the humanoid robot in an indoor maze using the BCI system with real-time images
from the camera on the robot's head. The results showed that three subjects
successfully navigated the indoor maze using the proposed brain-actuated
humanoid robot navigation system
