in this project we exploited the turtlebot3 burger robot understanding how to make it able to autonomously plan, navigate and localize itself by using the Robotics Operating System (ROS).

At the beginning, we set Gazebo and RViz to simulate the physical behavior of the robot and to start the visualization of what it sees through its sensors. In order to run it, do the following command in the project folder

catkin_make

source the package installation folder

source devel/setup.bash

and then start the task as

roslaunch first_task first_task.launch

After we have set Gazebo and RViz, we run the SLAM algorithm and the explore node in order to make the robot moving around the environment to build the map.
To launch the second task use the following command

roslaunch second_task second_task.launch

then, the robot starts moving around building the map of the house.

Supposing that the map is known, now the robot must localize itself without any prior knowledge on where it is exactly. In order to do that, we run the generalized montecarlo localization algorithm and the global localization services to spread particles all over the map.
To run the nodes use the following command

roslaunch third_task third_task.launch

Now, we have to run the send_goal node. In order to do that, we have to open the python executable in the specific package.
By using the command

roscd send_goal/src

now we are inside the package.
Then, to run the executable just digit

python3 send_goal.py

When the robot has finally localized itself in the map, we launch the last nodes to sanitize the rooms.
The environment is the same of the previous task, but with the following commands

roscd fourth_task/src
python3 send_goal_sanitization.py

and

python3 sanitization.py

the robot start moving to sanitize the selected rooms by visualizing the results in an OccupancyGrid.