Quad_BCI_EYE_TRACK_FUSION_EKF

Quadcopter Control Along with sensor fusion of EEG data along with eye tracking data using the Extended Kalman Filter

Eviacam was used for gazetracking , You can download the software using the steps given below-

sudo apt-get update
sudo apt-get install eviacam

All algorithms can be simulated on the px4 SITL simulator using Gazebo. https://github.com/PX4/sitl_gazebo

For setting up your workspace and execution of files;

STEP 1: installation of mavros

Install all dependencies

sudo apt install -y \
	ninja-build \
	exiftool \
	python-argparse \
	python-empy \
	python-toml \
	python-numpy \
	python-yaml \
	python-dev \
	python-pip \
	ninja-build \
	protobuf-compiler \
	libeigen3-dev \
	genromfs

pip install \
	pandas \
	jinja2 \
	pyserial \
	cerberus \
	pyulog \
	numpy \
	toml \
	pyquaternion

Create a new workspace:

mkdir -p ~/catkin_ws/src

and run the following commands:

cd ~/catkin_ws
catkin init && wstool init src

rosinstall_generator --rosdistro kinetic mavlink | tee /tmp/mavros.rosinstall
rosinstall_generator --upstream mavros | tee -a /tmp/mavros.rosinstall

wstool merge -t src /tmp/mavros.rosinstall
wstool update -t src -j4
rosdep install --from-paths src --ignore-src -y

sudo ./src/mavros/mavros/scripts/install_geographiclib_datasets.sh
sudo apt install ros-kinetic-catkin python-catkin-tools

cd ~/catkin_ws/src
git clone https://github.com/PX4/Firmware.git
cd Firmware
git checkout v1.8.0
make posix_sitl_default gazebo

catkin build

• Open a new terminal and type:

sudo gedit ~/.bashrc

• Add the following lines to the file that opens:

source ~/catkin_ws/devel/setup.bash
source ~/catkin_ws/src/Firmware/Tools/setup_gazebo.bash ~/catkin_ws/src/Firmware/ ~/catkin_ws/src/Firmware/build/posix_sitl_default
export ROS_PACKAGE_PATH=ROS_PACKAGE_PATH:~/catkin_ws/src/Firmware
export ROS_PACKAGE_PATH=ROS_PACKAGE_PATH:~/catkin_ws/src/Firmware/Tools/sitl_gazebo

• Create a new workspace and copy the codes into the 'src' directory The package can directly be placed in the /src of your package Run the scripts by

cd <workspace_name>
source devel/setup.bash
rosrun <package_name> <script_name.py>

FUTURE WORK

• Implement own DL agorithm for robust classification