Merge pull request #2504 from JdeRobot/issue-2494

Exercise docs update

_pages/exercises/AutonomousCars/autoparking.md

@@ -51,9 +51,9 @@ The objective of this practice is to implement the logic of a navigation algorit
 
 2. Pull the current distribution of Robotics Academy Docker Image:
 
-	```bash
-  docker pull jderobot/robotics-academy:latest
-  ```
+```bash
+docker pull jderobot/robotics-academy:latest
+```
 
 - In order to obtain optimal performance, Docker should be using multiple CPU cores. In case of Docker for Mac or Docker for Windows, the VM should be assigned a greater number of cores.
 
@@ -62,9 +62,9 @@ The objective of this practice is to implement the logic of a navigation algorit
 ### How to perform the exercises?
 - Start a new docker container of the image and keep it running in the background:
 
-	```bash
-  docker run --rm -it -p 7164:7164 -p 2303:2303 -p 1905:1905 -p 8765:8765 -p 6080:6080 -p 1108:1108 -p 7163:7163 jderobot/robotics-academy
-  ```
+```bash
+docker run --rm -it -p 7164:7164 -p 6080:6080 -p 1108:1108 -p 7163:7163 jderobot/robotics-academy
+```
 
 - On the local machine navigate to 127.0.0.1:7164/ in the browser and choose the desired exercise.
 
@@ -80,8 +80,8 @@ The objective of this practice is to implement the logic of a navigation algorit
 In the launched webpage, type your code in the text editor,
 
 ```python
-from GUI import GUI
-from HAL import HAL
+import GUI
+import HAL
 # Enter sequential code!
 
 
@@ -104,8 +104,8 @@ while True:
 
 ## Robot API
 
-* `from HAL import HAL` - to import the HAL(Hardware Abstraction Layer) library class. This class contains the functions that sends and receives information to and from the Hardware(Gazebo).
-* `from GUI import GUI` - to import the GUI(Graphical User Interface) library class. This class contains the functions used to view the debugging information, like image widgets.
+* `import HAL` - to import the HAL(Hardware Abstraction Layer) library class. This class contains the functions that sends and receives information to and from the Hardware(Gazebo).
+* `import GUI` - to import the GUI(Graphical User Interface) library class. This class contains the functions used to view the debugging information, like image widgets.
 * `HAL.getPose3d()` - to get all the position information
 * `HAL.getPose3d().x` - to get the position of the robot (x coordinate)
 * `HAL.getPose3d().y` - to get the position of the robot (y coordinate)

_pages/exercises/AutonomousCars/follow_line.md

@@ -74,9 +74,9 @@ This is the preferred way for running the exercise.
 
 2. Pull the current distribution of Robotics Academy Docker Image:
 
-	```bash
-  docker pull jderobot/robotics-academy:latest
-  ```
+```bash
+docker pull jderobot/robotics-academy:latest
+```
 
 - In order to obtain optimal performance, Docker should be using multiple CPU cores. In case of Docker for Mac or Docker for Windows, the VM should be assigned a greater number of cores.
 
@@ -85,9 +85,9 @@ This is the preferred way for running the exercise.
 ### How to perform the exercises?
 - Start a new docker container of the image and keep it running in the background:
 
-	```bash
-  docker run --rm -it -p 7164:7164 -p 2303:2303 -p 1905:1905 -p 8765:8765 -p 6080:6080 -p 1108:1108 -p 7163:7163 jderobot/robotics-academy
-  ```
+```bash
+docker run --rm -it -p 7164:7164 -p 6080:6080 -p 1108:1108 -p 7163:7163 jderobot/robotics-academy
+```
 
 - On the local machine navigate to 127.0.0.1:7164/ in the browser and choose the desired exercise.
 
@@ -103,8 +103,8 @@ This is the preferred way for running the exercise.
 In the launched webpage, type your code in the text editor,
 
 ```python
-from GUI import GUI
-from HAL import HAL
+import GUI
+import HAL
 # Enter sequential code!
 
 
@@ -126,8 +126,8 @@ while True:
 
 ## Robot API
 
-* `from HAL import HAL` - to import the HAL(Hardware Abstraction Layer) library class. This class contains the functions that sends and receives information to and from the Hardware(Gazebo).
-* `from GUI import GUI` - to import the GUI(Graphical User Interface) library class. This class contains the functions used to view the debugging information, like image widgets.
+* `import HAL` - to import the HAL(Hardware Abstraction Layer) library class. This class contains the functions that sends and receives information to and from the Hardware(Gazebo).
+* `import GUI` - to import the GUI(Graphical User Interface) library class. This class contains the functions used to view the debugging information, like image widgets.
 * `HAL.getImage()` - to get the image
 * `HAL.setV()` - to set the linear speed
 * `HAL.setW()` - to set the angular velocity

_pages/exercises/Drones/rescue_people.md

@@ -43,9 +43,9 @@ This is the preferred way for running the exercise.
 
 2. Pull the current distribution of Robotics Academy Docker Image:
 
-	```bash
-  docker pull jderobot/robotics-academy:latest
-  ```
+```bash
+docker pull jderobot/robotics-academy:latest
+```
 
 - In order to obtain optimal performance, Docker should be using multiple CPU cores. In case of Docker for Mac or Docker for Windows, the VM should be assigned a greater number of cores.
 
@@ -54,9 +54,9 @@ This is the preferred way for running the exercise.
 ### How to perform the exercises?
 - Start a new docker container of the image and keep it running in the background:
 
-	```bash
-  docker run --rm -it -p 7164:7164 -p 2303:2303 -p 1905:1905 -p 8765:8765 -p 6080:6080 -p 1108:1108 -p 7163:7163 jderobot/robotics-academy
-  ```
+```bash
+docker run --rm -it -p 7164:7164 -p 6080:6080 -p 1108:1108 -p 7163:7163 jderobot/robotics-academy
+```
 
 - On the local machine navigate to 127.0.0.1:7164/ in the browser and choose the desired exercise.
 
@@ -69,8 +69,9 @@ This is the preferred way for running the exercise.
 
 ### Optional: Store terminal output
 - To store the terminal output of manager.py and launch.py to a file execute the following docker run command and keep it running in the background:
+
 ```bash
-docker run -it --rm -v $HOME/.roboticsacademy/log/:/root/.roboticsacademy/log/ --device /dev/dri -p 7164:7164 -p 2303:2303 -p 1905:1905 -p 8765:8765 -p 6080:6080 -p 1108:1108 -p 2304:2304 -p 1904:1904 jderobot/robotics-academy --logs
+docker run -it --rm -v $HOME/.roboticsacademy/log/:/root/.roboticsacademy/log/ --device /dev/dri -p 7164:7164 -p 6080:6080 -p 1108:1108 -p 7163:7163 jderobot/robotics-academy --logs
 ```
 
 - The log files will be stored inside `$HOME/.roboticsacademy/{year-month-date-hours-mins}/`. After the session, use `more` to view the logs, for example:
@@ -83,8 +84,8 @@ more $HOME/.roboticsacademy/log/2021-11-06-14-45/manager.log
 In the launched webpage, type your code in the text editor,
 
 ```python
-from GUI import GUI
-from HAL import HAL
+import GUI
+import HAL
 # Enter sequential code!
 
 while True:
@@ -111,8 +112,8 @@ Please note that the code executing process might take a few seconds.
 
 ## Robot API
 
-* `from HAL import HAL` - to import the HAL(Hardware Abstraction Layer) library class. This class contains the functions that sends and receives information to and from the Hardware(Gazebo).
-* `from GUI import GUI` - to import the GUI(Graphical User Interface) library class. This class contains the functions used to view the debugging information, like image widgets.
+* `import HAL` - to import the HAL(Hardware Abstraction Layer) library class. This class contains the functions that sends and receives information to and from the Hardware(Gazebo).
+* `import GUI` - to import the GUI(Graphical User Interface) library class. This class contains the functions used to view the debugging information, like image widgets.
 
 ### Sensors and drone state
 

_pages/exercises/MobileRobots/amazon_warehouse.md

@@ -65,9 +65,9 @@ This is the preferred way for running the exercise.
 
 2. Pull the current distribution of Robotics Academy Docker Image:
 
-	```bash
-  docker pull jderobot/robotics-academy:latest
-  ```
+```bash
+docker pull jderobot/robotics-academy:latest
+```
 
 - In order to obtain optimal performance, Docker should be using multiple CPU cores. In case of Docker for Mac or Docker for Windows, the VM should be assigned a greater number of cores.
 
@@ -76,9 +76,9 @@ This is the preferred way for running the exercise.
 ### How to perform the exercises?
 - Start a new docker container of the image and keep it running in the background:
 
-	```bash
-  docker run --rm -it -p 7164:7164 -p 2303:2303 -p 1905:1905 -p 8765:8765 -p 6080:6080 -p 1108:1108 -p 7163:7163 jderobot/robotics-academy
-  ```
+```bash
+docker run --rm -it -p 7164:7164 -p 6080:6080 -p 1108:1108 -p 7163:7163 jderobot/robotics-academy
+```
 
 - On the local machine navigate to 127.0.0.1:7164/ in the browser and choose the desired exercise.
 
@@ -94,8 +94,8 @@ This is the preferred way for running the exercise.
 In the launched webpage, type your code in the text editor,
 
 ```python
-from GUI import GUI
-from HAL import HAL
+import GUI
+import HAL
 # Enter sequential code!
 
 
@@ -115,8 +115,8 @@ while True:
 
 ## Robot API
 
-* `from HAL import HAL` - to import the HAL(Hardware Abstraction Layer) library class. This class contains the functions that sends and receives information to and from the Hardware (Gazebo).
-* `from GUI import GUI` - to import the GUI (Graphical User Interface) library class. This class contains the functions used to view the debugging information, like image widgets.
+* `import HAL` - to import the HAL(Hardware Abstraction Layer) library class. This class contains the functions that sends and receives information to and from the Hardware (Gazebo).
+* `import GUI` - to import the GUI (Graphical User Interface) library class. This class contains the functions used to view the debugging information, like image widgets.
 * `HAL.getPose3d()` - returns x,y and theta components of the robot in world coordinates
 * `HAL.setV()` - to set the linear speed
 * `HAL.setW()` - to set the angular speed

_pages/exercises/MobileRobots/follow_person.md

@@ -77,47 +77,43 @@ This is the preferred way for running the exercise.
 ### Installation 
 - Clone the Robotics Academy repository on your local machine
 
-	```bash
+```bash
 git clone https://github.com/JdeRobot/RoboticsAcademy
-	```
+```
 
 - Download [Docker](https://docs.docker.com/get-docker/). Windows users should choose WSL 2 backend Docker installation if possible, as it has better performance than Hyper-V.
 
 - Pull the current distribution of Robotics Academy Docker Image
 
-	```bash
+```bash
 docker pull jderobot/robotics-academy:latest
-	```
+```
 
 - In order to obtain optimal performance, Docker should be using multiple CPU cores. In case of Docker for Mac or Docker for Windows, the VM should be assigned a greater number of cores.
 
-### Enable GPU Acceleration
-- For Linux machines with NVIDIA GPUs, acceleration can be enabled by using NVIDIA proprietary drivers, installing  [VirtualGL](https://virtualgl.org/) and executing the following docker run command:
-  ```bash
-  docker run --rm -it --device /dev/dri -p 2303:2303 -p 1905:1905 -p 8765:8765 -p 6080:6080 -p 6081:6081 -p 1108:1108 -p 6082:6082 -p 7163:7163 -p 7164:7164 jderobot/robotics-academy:latest 
-  ```
-
-
-- For Windows machines, GPU acceleration to Docker is an experimental approach and can be implemented as per instructions given [here](https://www.docker.com/blog/wsl-2-gpu-support-is-here/)
-
 ### How to perform the exercise?
-- Start a new docker container of the image and keep it running in the background ([hardware accelerated version](#enable-gpu-acceleration))
+- Start a new docker container of the image and keep it running in the background:
 
-	```bash
-  docker run --rm -it -p 2303:2303 -p 1905:1905 -p 8765:8765 -p 6080:6080 -p 6081:6081 -p 1108:1108 -p 6082:6082 -p 7163:7163 -p 7164:7164 jderobot/robotics-academy:latest
-  ```
+```bash
+docker run --rm -it -p 7164:7164 -p 6080:6080 -p 1108:1108 -p 7163:7163 jderobot/robotics-academy
+```
 
 - On the local machine navigate to 127.0.0.1:7164/ in the browser and choose the desired exercise.
 
-- The exercise can be used after both *Connected* and *Ready* turn green. 
+- Wait for the Connect button to turn green and display "Connected". Click on the "Launch" button and wait for some time until an alert appears with the message `Connection Established` and button displays "Ready". 
+
+- The exercise can be used after the alert.
+
+### Enable GPU Acceleration
+- Follow the advanced launching instructions from [here](https://jderobot.github.io/RoboticsAcademy/user_guide/#enable-gpu-acceleration).
 
 **Where to insert the code?**
 
 In the webpage, type your code in the text editor,
 
 ```python
-from GUI import GUI
-from HAL import HAL
+import GUI
+import HAL
 # Enter sequential code!
 
 
@@ -144,8 +140,8 @@ The robot that we will use is a Turtlebot2 (a circular mobile robot) implemented
 
 ## Robot API
 
-* `from HAL import HAL` - to import the HAL(Hardware Abstraction Layer) library class. This class contains the functions that sends and receives information to and from the Hardware(Gazebo).
-* `from GUI import GUI` - to import the GUI(Graphical User Interface) library class. This class contains the functions used to view the debugging information, like image widgets.
+* `import HAL` - to import the HAL(Hardware Abstraction Layer) library class. This class contains the functions that sends and receives information to and from the Hardware(Gazebo).
+* `import GUI` - to import the GUI(Graphical User Interface) library class. This class contains the functions used to view the debugging information, like image widgets.
 * `HAL.getImage()` - to obtain the current frame of the camera robot.
 * `HAL.getPose3d().x` - to get the position of the robot (x coordinate)
 * `HAL.getPose3d().y` - to obtain the position of the robot (y coordinate)

_pages/exercises/MobileRobots/montecarlo_laser_loc.md

@@ -68,7 +68,7 @@ docker pull jderobot/robotics-academy:latest
 - Start a new docker container of the image and keep it running in the background.
 
 ```bash
-docker run --rm -it -p 7164:7164 -p 2303:2303 -p 1905:1905 -p 8765:8765 -p 6080:6080 -p 1108:1108 jderobot/robotics-academy 
+docker run --rm -it -p 7164:7164 -p 6080:6080 -p 1108:1108 -p 7163:7163 jderobot/robotics-academy 
 ```   
 
 - On the local machine navigate to 127.0.0.1:7164/ in the browser and choose the desired exercise.
@@ -77,14 +77,16 @@ docker run --rm -it -p 7164:7164 -p 2303:2303 -p 1905:1905 -p 8765:8765 -p 6080:
 
 - The exercise can be used after the alert.
 
+### Enable GPU Acceleration
+- Follow the advanced launching instructions from [here](https://jderobot.github.io/RoboticsAcademy/user_guide/#enable-gpu-acceleration).
 
 **Where to insert the code?**
 
 In the launched webpage, type your code in the text editor,
 
 ```python
-from GUI import GUI
-from HAL import HAL
+import GUI
+import HAL
 # Enter sequential code!
 
 
@@ -104,8 +106,8 @@ while True:
 
 ## Robot API
 
-* `from HAL import HAL` - to import the HAL library class. This class contains the functions that receives information from the webcam.
-* `from GUI import GUI` - to import the GUI (Graphical User Interface) library class. This class contains the functions used to view the debugging information, like image widgets.
+* `import HAL` - to import the HAL library class. This class contains the functions that receives information from the webcam.
+* `import GUI` - to import the GUI (Graphical User Interface) library class. This class contains the functions used to view the debugging information, like image widgets.
 * `HAL.setW()` - to set the angular velocity 
 * `HAL.setV()` - to set the linear velocity 
 * `GUI.showParticles(particles)` - shows the particles on the map. Accepts a list of particles as an argument. Each particle must be a list with [position_x, position_y, angle_in_radians]. The values must be in gazebo world coordinate system.  

_pages/exercises/MobileRobots/vacuum_cleaner.md

@@ -33,9 +33,9 @@ This is the preferred way for running the exercise.
 
 2. Pull the current distribution of Robotics Academy Docker Image:
 
-	```bash
-  docker pull jderobot/robotics-academy:latest
-  ```
+```bash
+docker pull jderobot/robotics-academy:latest
+```
 
 - In order to obtain optimal performance, Docker should be using multiple CPU cores. In case of Docker for Mac or Docker for Windows, the VM should be assigned a greater number of cores.
 
@@ -44,9 +44,9 @@ This is the preferred way for running the exercise.
 ### How to perform the exercises?
 - Start a new docker container of the image and keep it running in the background:
 
-	```bash
-  docker run --rm -it -p 7164:7164 -p 2303:2303 -p 1905:1905 -p 8765:8765 -p 6080:6080 -p 1108:1108 -p 7163:7163 jderobot/robotics-academy
-  ```
+```bash
+docker run --rm -it -p 7164:7164 -p 6080:6080 -p 1108:1108 -p 7163:7163 jderobot/robotics-academy
+```
 
 - On the local machine navigate to 127.0.0.1:7164/ in the browser and choose the desired exercise.
 
@@ -62,8 +62,8 @@ This is the preferred way for running the exercise.
 In the launched webpage, type your code in the text editor,
 
 ```python
-from GUI import GUI
-from HAL import HAL
+import GUI
+import HAL
 # Enter sequential code!
 
 
@@ -83,8 +83,8 @@ while True:
 
 ## Robot API
 
-* `from HAL import HAL` - to import the HAL(Hardware Abstraction Layer) library class. This class contains the functions that sends and receives information to and from the Hardware(Gazebo).
-* `from GUI import GUI` - to import the GUI(Graphical User Interface) library class. This class contains the functions used to view the debugging information, like image widgets.
+* `import HAL` - to import the HAL(Hardware Abstraction Layer) library class. This class contains the functions that sends and receives information to and from the Hardware(Gazebo).
+* `import GUI` - to import the GUI(Graphical User Interface) library class. This class contains the functions used to view the debugging information, like image widgets.
 * `HAL.getBumperData().state` - To establish if the robot has crashed or not. Returns a 1 if the robot collides and a 0 if it has not crashed.
 * `HAL.getBumperData().bumper` - If the robot has crashed, it turns to 1 when the crash occurs at the center of the robot, 0 when it occurs at its left and 2 if the collision is at its right.
 * `HAL.getPose3d().x` - to get the position of the robot (x coordinate)