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    # Lab 5: A basic office assistant
    The goal of this lab is to provide the basic structure of the group work. In this lab, you will create a basic robot that fulfills the goals of an office assistant:
    * Create a `text_to_goals` node that converts a text command into a goal request. It should listen to a string and output a set of goals.
    * Create a `decision` node, it takes the set of goals and request execution of TST by the `tst_executor` of [Lab3](lab3).
    You should create a new package `air_lab5`, you should check [Lab2](lab2) or [Lab3](lab3) for how to create a package.
    ## New map and database of points
    For this lab we use a different map, that simulates an office environment, and it looks like this:
    ![simple_sim_office_1](uploads/8de8712c86793e539c10523b402ec6df/simple_sim_office_1.png)
    To load that world in the simulator you should use the following command:
    ```shell
    ros2 launch air_bringup turtle.launch.py world:=office_1
    ```
    Ideally, your robot should explore that environment to extract all the interesting points. You will have to do that in the project phase, but for this lab we provide you with the ground truth. You can generate a `turtle` file to insert the data in the database with:
    ```shell
    ros2 run air_simple_sim generate_rdf office_1
    ```
    You will notice that we have more object classes, such as `office` or `vendingmachine`. Additionally, objects have `tags` that indicate the name of a person or the content of the vending machine.
    To insert the file in the database, simply run:
    ```shell
    ros2 service call /kdb_server/insert_triples ros2_kdb_interfaces/srv/InsertTriples "
    graphname: 'semanticobject'
    format: 'ttl'
    content: '`ros2 run air_simple_sim generate_rdf office_1`'"
    ```
    ## Goal Message
    In your `air_lab_interfaces`, you should add two new messages ``Goal` and `Goals`. They will be used to communicate goals between `text_to_goals` and `decision`. You can check the [official tutorial](https://docs.ros.org/en/galactic/Tutorials/Beginner-Client-Libraries/Custom-ROS2-Interfaces.html) to know how to create custom messages.
    `Goal` should have the following fields:
    * `type` (`string`) the type of goal with possible values `goto`, `bring`, `explore`...
    * `object` (`string`) the object, can be empty, or for example, for `bring` goal type it could be coffee, sandwich...
    * `destination` (`string`) the destination of the goal, or who to bring the `object` to,...
    `Goals` should have the following field:
    * `goals` (list of `Goal`) a list of goals.
    Note that in this lab, we use a single `Goal` but it may be that for your project you will want to handle multiple goals.
    ## Text Command Node
    This node should listen to a topic called `text_command` of type `std_msgs/string`, parse the commands and output a set of goals on a topic called `goals_requests` of type `air_lab_interfaces/Goals`.
    Examples of text commands:
    * "Goto George" this should lead to a `Goal` with field `{ type: "goto", destination: "George" }`
    * "Bring Coffee" this should lead to a `Goal` with field `{ type: "bring", object: "Coffee", destination: "User" }`. In this case you can assume that user is at the current robot position.
    * "Explore!" this should lead to a `Goal` with field `{ type: "explore" }`
    You can use `ros2 topic` to publish the text command.
    ## Decision Node
    This node should listen to the topic `goals_requests` and generate TSTs as a file (like you did in [Lab4](lab4)). Then you should start the execution of the TST from your decision node. You should query the database for destination, using the `tags`. You should look at the data that was inserted in the database, and you should extend the queries of `Lab4` to include this tag.
    ## Demonstration
    * Use the following command:
    - "Goto Annika" should make the robot go to Annika's office
    - "Bring Coffee" should make the robot go to a vending machine with coffee and bring it back
    - "Explore!" should make the robot start random exploration
    # Lab 5: A basic office assistant
    The goal of this lab is to provide the basic structure of the group work. In this lab, you will create a basic robot that fulfills the goals of an office assistant:
    * Create a `text_to_goals` node that converts a text command into a goal request. It should listen to a string and output a set of goals.
    * Create a `decision` node, it takes the set of goals and request execution of TST by the `tst_executor` of [Lab3](lab3).
    You should create a new package `air_lab5`, you should check [Lab2](lab2) or [Lab3](lab3) for how to create a package.
    ## New map and database of points
    For this lab we use a different map, that simulates an office environment, and it looks like this:
    ![simple_sim_office_1](uploads/8de8712c86793e539c10523b402ec6df/simple_sim_office_1.png)
    To load that world in the simulator you should use the following command:
    ```shell
    ros2 launch air_bringup turtle.launch.py world:=office_1
    ```
    Ideally, your robot should explore that environment to extract all the interesting points. You will have to do that in the project phase, but for this lab we provide you with the ground truth. You can generate a `turtle` file to insert the data in the database with:
    ```shell
    ros2 run air_simple_sim generate_rdf office_1
    ```
    You will notice that we have more object classes, such as `office` or `vendingmachine`. Additionally, objects have `tags` that indicate the name of a person or the content of the vending machine.
    To insert the file in the database, simply run:
    ```shell
    ros2 service call /kdb_server/insert_triples ros2_kdb_interfaces/srv/InsertTriples "
    graphname: 'semanticobject'
    format: 'ttl'
    content: '`ros2 run air_simple_sim generate_rdf office_1`'"
    ```
    ## Goal Message
    In your `air_lab_interfaces`, you should add two new messages ``Goal` and `Goals`. They will be used to communicate goals between `text_to_goals` and `decision`. You can check the [official tutorial](https://docs.ros.org/en/humble/Tutorials/Beginner-Client-Libraries/Custom-ROS2-Interfaces.html) to know how to create custom messages.
    `Goal` should have the following fields:
    * `type` (`string`) the type of goal with possible values `goto`, `bring`, `explore`...
    * `object` (`string`) the object, can be empty, or for example, for `bring` goal type it could be coffee, sandwich...
    * `destination` (`string`) the destination of the goal, or who to bring the `object` to,...
    `Goals` should have the following field:
    * `goals` (list of `Goal`) a list of goals.
    Note that in this lab, we use a single `Goal` but it may be that for your project you will want to handle multiple goals.
    ## Text Command Node
    This node should listen to a topic called `text_command` of type `std_msgs/string`, parse the commands and output a set of goals on a topic called `goals_requests` of type `air_lab_interfaces/Goals`.
    Examples of text commands:
    * "Goto George" this should lead to a `Goal` with field `{ type: "goto", destination: "George" }`
    * "Bring Coffee" this should lead to a `Goal` with field `{ type: "bring", object: "Coffee", destination: "User" }`. In this case you can assume that user is at the current robot position.
    * "Explore!" this should lead to a `Goal` with field `{ type: "explore" }`
    You can use `ros2 topic` to publish the text command.
    ## Decision Node
    This node should listen to the topic `goals_requests` and generate TSTs as a file (like you did in [Lab4](lab4)). Then you should start the execution of the TST from your decision node. You should query the database for destination, using the `tags`. You should look at the data that was inserted in the database, and you should extend the queries of `Lab4` to include this tag.
    ## Demonstration
    * Use the following command:
    - "Goto Annika" should make the robot go to Annika's office
    - "Bring Coffee" should make the robot go to a vending machine with coffee and bring it back
    - "Explore!" should make the robot start random exploration