Merge pull request #2847 from JdeRobot/change-resources-links

Change resources links

_pages/exercises/AutonomousCars/global_navigation.md

@@ -190,9 +190,9 @@ while True:
 * `GUI.showNumpy(numpy)` - shows Gradient Path Planning field on the user interface. It represents the values of the field that have been assigned to the array passed as a parameter. Accepts as input a two-dimensional uint8 numpy array whose values can range from 0 to 255 (grayscale). In order to have a grid with the same resolution as the map, the array should be 400x400
 * `GUI.showPath(array)` - shows a path on the map. The parameter should be a 2D array containing each of the points of the path
 * `GUI.getTargetPose()` - returns x,y coordinates of chosen destionation in the world. Destination is set by clicking on the map image
-* `GUI.getMap(url)` - - Returns a numpy array with the image data in grayscale as a 2 dimensional array. The URL of the Global Navigation map is '/RoboticsAcademy/exercises/static/exercises/global_navigation_newmanager/resources/images/cityLargenBin.png', so the instruction to get the map is
+* `GUI.getMap(url)` - - Returns a numpy array with the image data in grayscale as a 2 dimensional array. The URL of the Global Navigation map is '/resources/exercises/global_navigation/images/cityLargenBin.png', so the instruction to get the map is
 ```
-array = GUI.getMap('/RoboticsAcademy/exercises/static/exercises/global_navigation_newmanager/resources/images/cityLargenBin.png')
+array = GUI.getMap('/resources/exercises/global_navigation/images/cityLargenBin.png')
 ```
 * `GUI.rowColumn(vector)` - returns the index in map coordinates corresponding to the vector in world coordinates passed as parameter
 

_pages/exercises/MobileRobots/amazon_warehouse.md

@@ -185,8 +185,8 @@ There are two robots to choose from:
 {% include gallery id="ackermannrobot" caption="Ackemann robot" %}
 
 * You can get the robot's mesh files from there: 
-  * Only robot: /RoboticsAcademy/exercises/static/exercises/amazon_warehouse_newmanager/resources/meshes/robot/ackermann_robot.dae
-  * Robot lifting a shelf: /RoboticsAcademy/exercises/static/exercises/amazon_warehouse_newmanager/resources/meshes/robot/ackermann_robot_lifting_shelf.dae
+  * Only robot: /resources/exercises/amazon_warehouse/meshes/robot/ackermann_robot.dae
+  * Robot lifting a shelf: /resources/exercises/amazon_warehouse/meshes/robot/ackermann_robot_lifting_shelf.dae
 
 {% include gallery id="ackermannrobotmesh" caption="Ackemann robot meshes" %}
 
@@ -195,18 +195,18 @@ There are two warehouses to choose from:
 #### Warehouse 1:
 * The warehouse size is 20.62 meters long and 13.6 meters wide.
 * The shelves coordinates from 1 to 6 are: (3.728, 0.579), (3.728, -1.242), (3.728, -3.039), (3.728, -4.827), (3.728, -6.781), (3.728, -8.665).
-* You can get the warehouse's map from there: /RoboticsAcademy/exercises/static/exercises/amazon_warehouse_newmanager/resources/images/map.png
+* You can get the warehouse's map from there: /resources/exercises/amazon_warehouse/images/map.png
 
 {% include gallery id="warehouse1" caption="Warehouse 1" %}
 
 * You can get the warehouse's mesh file from there: 
-  * Warehouse without shelves: /RoboticsAcademy/exercises/static/exercises/amazon_warehouse_newmanager/resources/meshes/world/warehouse1/warehouse1.dae
-  * Shelf 1: /RoboticsAcademy/exercises/static/exercises/amazon_warehouse_newmanager/resources/meshes/world/warehouse1/shelf1.dae
-  * Shelf 2: /RoboticsAcademy/exercises/static/exercises/amazon_warehouse_newmanager/resources/meshes/world/warehouse1/shelf2.dae
-  * Shelf 3: /RoboticsAcademy/exercises/static/exercises/amazon_warehouse_newmanager/resources/meshes/world/warehouse1/shelf3.dae
-  * Shelf 4: /RoboticsAcademy/exercises/static/exercises/amazon_warehouse_newmanager/resources/meshes/world/warehouse1/shelf4.dae
-  * Shelf 5: /RoboticsAcademy/exercises/static/exercises/amazon_warehouse_newmanager/resources/meshes/world/warehouse1/shelf5.dae
-  * Shelf 6: /RoboticsAcademy/exercises/static/exercises/amazon_warehouse_newmanager/resources/meshes/world/warehouse1/shelf6.dae
+  * Warehouse without shelves: /resources/exercises/amazon_warehouse/meshes/world/warehouse1/warehouse1.dae
+  * Shelf 1: /resources/exercises/amazon_warehouse/meshes/world/warehouse1/shelf1.dae
+  * Shelf 2: /resources/exercises/amazon_warehouse/meshes/world/warehouse1/shelf2.dae
+  * Shelf 3: /resources/exercises/amazon_warehouse/meshes/world/warehouse1/shelf3.dae
+  * Shelf 4: /resources/exercises/amazon_warehouse/meshes/world/warehouse1/shelf4.dae
+  * Shelf 5: /resources/exercises/amazon_warehouse/meshes/world/warehouse1/shelf5.dae
+  * Shelf 6: /resources/exercises/amazon_warehouse/meshes/world/warehouse1/shelf6.dae
 
 {% include gallery id="warehouse1mesh" caption="Warehouse 1 meshes" %}
 
@@ -216,7 +216,7 @@ There are two warehouses to choose from:
 * The warehouse size is 34 meters long and 22 meters wide.
 * The shelves coordinates from 1 to 9 are: (-8.5, -6.0), (-1.5, -6.0), (5.5, -6.0), (-8.5, 4.0), (-1.5, 4.0), (5.5, 4.0), (-1.0, -15.5), (-2, 11.5), (1.0, -15.0).
   * The separation distance between two neighboring shelves is 2 m on the x axis and 1.5 m on the y axis.
-* You can get the warehouse's map from there: /RoboticsAcademy/exercises/static/exercises/amazon_warehouse_newmanager/resources/images/map_2.png
+* You can get the warehouse's map from there: /resources/exercises/amazon_warehouse/images/map_2.png
 
 {% include gallery id="warehouse2" caption="Warehouse 2" %}
 

_pages/exercises/MobileRobots/montecarlo_laser_loc.md

@@ -70,7 +70,7 @@ The aim of this practical is to develop a localization algorithm based on the pa
 * `GUI.getMap(url)` - Returns a numpy array with the image data in a 3 dimensional array (R, G, B, A). The image is 1012x1012. The instruction to get the map is
 
 ```python
-array = GUI.getMap('/RoboticsAcademy/exercises/static/exercises/montecarlo_laser_loc/resources/mapgrannyannie.png')
+array = GUI.getMap('/resources/exercises/montecarlo_laser_loc/mapgrannyannie.png')
 ```
 
 ### Types conversion

_pages/exercises/MobileRobots/vacuum_cleaner_loc.md

@@ -55,9 +55,9 @@ y = np.random.randint(0, 391)
 matrix[x:x+10, y:y+10] = 128
 GUI.showNumpy(matrix)
 ```
-* `GUI.getMap(url)` - Returns a numpy array with the image data in a 3 dimensional array (R, G, B, A). The URL of the Vacuum Cleaner Loc map is '/RoboticsAcademy/exercises/static/exercises/vacuum_cleaner_loc_newmanager/resources/mapgrannyannie.png', so the instruction to get the map is
+* `GUI.getMap(url)` - Returns a numpy array with the image data in a 3 dimensional array (R, G, B, A). The URL of the Vacuum Cleaner Loc map is '/resources/exercises/vacuum_cleaner_loc/mapgrannyannie.png', so the instruction to get the map is
 ```
-array = GUI.getMap('/RoboticsAcademy/exercises/static/exercises/vacuum_cleaner_loc_newmanager/resources/images/mapgrannyannie.png')
+array = GUI.getMap('/resources/exercises/vacuum_cleaner_loc/mapgrannyannie.png')
 ```
 
 For this example, it is necessary to ensure that the vacuum cleaner covers the highest possible percentage of the house. The application of the automatic evaluator (referee) will measure the percentage traveled, and based on this percentage, will perform the qualification of the solution algorithm.