O3de in RADI

compose_cfg/dev_humble_cpu.yaml

@@ -26,9 +26,7 @@ services:
     ports:
       - "7164:7164"
       - "7163:7163"
-      - "6080:6080"
-      - "6081:6081"
-      - "1108:1108"
+      - "6080-6090:6080-6090"
     volumes:
       - type: bind
         source: ./

compose_cfg/dev_humble_gpu.yaml

@@ -28,9 +28,7 @@ services:
     ports:
       - "7164:7164"
       - "7163:7163"
-      - "6080:6080"
-      - "6081:6081"
-      - "1108:1108"
+      - "6080-6090:6080-6090"
     volumes:
       - type: bind
         source: ./

compose_cfg/dev_humble_nvidia.yaml

@@ -38,9 +38,7 @@ services:
         ports:
             - "7164:7164"
             - "7163:7163"
-            - "6080:6080"
-            - "6081:6081"
-            - "1108:1108"
+            - "6080-6090:6080-6090"
         volumes:
             - type: bind
               source: ./

compose_cfg/dev_humble_nvidia_windows.yaml

@@ -38,9 +38,7 @@ services:
     ports:
       - "7164:7164"
       - "7163:7163"
-      - "6080:6080"
-      - "6081:6081"
-      - "1108:1108"
+      - "6080-6090:6080-6090"
     volumes:
       - type: bind
         source: ./

compose_cfg/user_humble_cpu.yaml

@@ -21,9 +21,7 @@ services:
     ports:
       - "7164:7164"
       - "7163:7163"
-      - "6080:6080"
-      - "6081:6081"
-      - "1108:1108"
+      - "6080-6090:6080-6090"
     tty: true
     stdin_open: true
     depends_on: 

compose_cfg/user_humble_gpu.yaml

@@ -23,9 +23,7 @@ services:
     ports:
       - "7164:7164"
       - "7163:7163"
-      - "6080:6080"
-      - "6081:6081"
-      - "1108:1108"
+      - "6080-6090:6080-6090"
     tty: true
     stdin_open: true
     depends_on: 

compose_cfg/user_humble_nvidia.yaml

@@ -32,9 +32,7 @@ services:
     ports:
       - "7164:7164"
       - "7163:7163"
-      - "6080:6080"
-      - "6081:6081"
-      - "1108:1108"
+      - "6080-6090:6080-6090"
     tty: true
     stdin_open: true
     depends_on: 

compose_cfg/user_humble_nvidia_windows.yaml

@@ -35,9 +35,7 @@ services:
     ports:
       - "7164:7164"
       - "7163:7163"
-      - "6080:6080"
-      - "6081:6081"
-      - "1108:1108"
+      - "6080-6090:6080-6090"
     volumes:
       - /usr/lib/wsl:/usr/lib/wsl
     tty: true

database/exercises/db.sql

@@ -112,7 +112,7 @@ ADD GENERATED BY DEFAULT AS IDENTITY (
 --
 
 COPY public.exercises (id, exercise_id, name, description, tags, status, url) FROM stdin;
-1	follow_line	Follow Line	The goal of this exercise is to perform a PID reactive control capable of following the line painted on the racing circuit.	["ROS2","AUTONOMOUS DRIVING"]	ACTIVE	https://jderobot.github.io/RoboticsAcademy/exercises/AutonomousCars/follow_line/
+1	follow_line	Follow Line	The goal of this exercise is to perform a PID reactive control capable of following the line painted on the racing circuit.	["ROS2","AUTONOMOUS DRIVING", "MULTILANGUAGE"]	ACTIVE	https://jderobot.github.io/RoboticsAcademy/exercises/AutonomousCars/follow_line/
 2	vacuum_cleaner	Vacum Cleaner	Vacuum Cleaner exercise using React and RAM	["ROS2"]	ACTIVE	https://jderobot.github.io/RoboticsAcademy/exercises/MobileRobots/vacuum_cleaner
 3	autoparking	Autoparking	Autoparking exercise testing	["AUTONOMOUS DRIVING","SERVICE ROBOTS","ROS2"]	ACTIVE	https://jderobot.github.io/RoboticsAcademy/exercises/AutonomousCars/autoparking
 4	follow_person	Follow Person	Follow a person with kobuki robot	["ROS2"]	ACTIVE	https://jderobot.github.io/RoboticsAcademy/exercises/MobileRobots/follow_person
@@ -133,9 +133,12 @@ COPY public.exercises (id, exercise_id, name, description, tags, status, url) FR
 19	human_detection	Human Detection	Deep learning-based human detection.	["Computer Vision","Deep Learning"]	ACTIVE	https://jderobot.github.io/RoboticsAcademy/exercises/ComputerVision/human_detection
 20	drone_gymkhana	Drone Gymkhana	Drone Gymkhana	["ROS2","Drones"]	ACTIVE	https://jderobot.github.io/RoboticsAcademy/exercises/Drones/drone_gymkhana
 21	power_tower_inspection	Power Tower Inspection	Power Tower inspection using drones	["ROS2","Drones"]	ACTIVE	https://jderobot.github.io/RoboticsAcademy/exercises/Drones/power_tower_inspection
-22	end_to_end_visual_control	End to End Visual Control	End to end visual control using deep learning.	["Computer Vision","Deep Learning","AUTONOMOUS DRIVING"]	PROTOTYPE	https://jderobot.github.io/RoboticsAcademy/exercises/AutonomousCars/end_to_end_visual_control
-23	digital_image_processing	Digital Image Processing Exercises	Digital Image Processing Exercises	["ROS2"]	PROTOTYPE	https://jderobot.github.io/RoboticsAcademy/exercises/ComputerVision/digital_image_processing
+22	end_to_end_visual_control	End to End Visual Control	End to end visual control using deep learning.	["Computer Vision","Deep Learning","AUTONOMOUS DRIVING"]	ACTIVE	https://jderobot.github.io/RoboticsAcademy/exercises/AutonomousCars/end_to_end_visual_control
+23	digital_image_processing	Digital Image Processing Exercises	Digital Image Processing Exercises	["ROS2"]	ACTIVE	https://jderobot.github.io/RoboticsAcademy/exercises/ComputerVision/digital_image_processing
 24	car_junction	Car Junction	Autonomous Navigation through traffic at road junction.	["AUTONOMOUS DRIVING","ROS2"]	PROTOTYPE	https://jderobot.github.io/RoboticsAcademy/exercises/AutonomousCars/car_junction
+25	machine_vision	Machine Vision	Machine Vision exercise	["ROS2"]	ACTIVE	https://jderobot.github.io/RoboticsAcademy/exercises/IndustrialRobots/machine_vision
+26	labyrinth_escape	Labyrinth Escape	Labyrinth Escape exercise	["ROS2","Drones"]	ACTIVE	https://jderobot.github.io/RoboticsAcademy/exercises/Drones/labyrinth_escape
+27	o3de_warehouse	O3DE Warehouse	Control an O3DE robot to organize a warehouse	["ROS2"]	PROTOTYPE	https://jderobot.github.io/RoboticsAcademy/exercises
 \.
 
 --
@@ -149,7 +152,7 @@ COPY public.exercises_universes (id, exercise_id, universe_id) FROM stdin;
 3	6	2
 4	7	31
 5	10	4
-6	1	5
+6	1	40
 7	1	6
 8	4	7
 9	4	8
@@ -183,6 +186,9 @@ COPY public.exercises_universes (id, exercise_id, universe_id) FROM stdin;
 37	20	36
 38	21	37
 39	24	35
+40	25	38
+41	26	39
+42	27	40
 \.
 -- 30  16  3
 
@@ -262,6 +268,15 @@ COPY public.exercises_tools (id, exercise_id, tool_id) FROM stdin;
 69	24	console
 70	24	simulator
 71	17	rviz
+72	25	console
+73	25	simulator
+74	25	rviz
+75	26	console
+76	26	simulator
+77	26	web_gui
+78	27	console
+79	27	simulator
+80	27	web_gui
 \.
 
 --

docs/InstructionsForDevelopers.md

@@ -124,19 +124,19 @@ Now you can launch Robotics Academy using the followings commands:
 * Automatic GPU selection
 
 ```bash
-docker run --rm -it $(nvidia-smi >/dev/null 2>&1 && echo "--gpus all" || echo "") --device /dev/dri -p 6080:6080 -p 6081:6081 -p 1108:1108 -p 7163:7163 -p 7164:7164 --link academy_db jderobot/robotics-academy:latest
+docker run --rm -it $(nvidia-smi >/dev/null 2>&1 && echo "--gpus all" || echo "") --device /dev/dri -p 6080-6090:6080-6090 -p 7163:7163 -p 7164:7164 --link academy_db jderobot/robotics-academy:latest
 ```
 
 * Automatic GPU selection (Without Nvidia)
 
 ```bash
-docker run --rm -it --device /dev/dri -p 6080:6080 -p 6081:6081 -p 1108:1108 -p 7163:7163 -p 7164:7164 --link academy_db jderobot/robotics-academy:latest
+docker run --rm -it --device /dev/dri -p 6080-6090:6080-6090 -p 7163:7163 -p 7164:7164 --link academy_db jderobot/robotics-academy:latest
 ```
 
 * Only CPU
 
 ```bash
-docker run --rm -it -p 6080:6080 -p 6081:6081 -p 1108:1108 -p 7163:7163 -p 7164:7164 --link academy_db jderobot/robotics-academy:latest
+docker run --rm -it -p 6080-6090:6080-6090 -p 7163:7163 -p 7164:7164 --link academy_db jderobot/robotics-academy:latest
 ```
 
 [how to generate a RADI]: ./generate_a_radi.md
@@ -446,7 +446,7 @@ An exercise entry in the database must include the following data:
 
 2. On Terminal open the directory where your project or code is located at (Example:- ```cd ~/my_project```)
 
-3. Append ```-v $(pwd):/location_in_radi``` to your ```docker run``` cli command used to run your container. (Example:- ```docker run --rm -it $(nvidia-smi >/dev/null 2>&1 && echo "--gpus all" || echo "") --device /dev/dri -p 7164:7164 -p 6080:6080 -p 6081:6081 -p 1108:1108 -p 7163:7163 jderobot/robotics-backend -v $(pwd):/home jderobot/robotics-academy```)
+3. Append ```-v $(pwd):/location_in_radi``` to your ```docker run``` cli command used to run your container. (Example:- ```docker run --rm -it $(nvidia-smi >/dev/null 2>&1 && echo "--gpus all" || echo "") --device /dev/dri -p 7164:7164 -p 6080-6090:6080-6090 -p 7163:7163 jderobot/robotics-backend -v $(pwd):/home jderobot/robotics-academy```)
 
 4. This will import your local directory inside the docker container, if you have used the example command like above where the location the command is being run is mounted to the home folder inside the docker container you will simply be able to see all the local mounted directories inside the /home of the RoboticsBackend.
 

docs/develop_binding_volumes.md

@@ -8,6 +8,6 @@ A fast way of development and testing can be achieved using volume binding. This
 This method consists in replicating the folder /RoboticsAcademy that exists inside the RoboticsBackend on your local drive. Then, a docker run option is used to replace the RoboticsBackend folder with your local folder. By doing so, all the changes made on your local files are included in the container. Note that this is a shared volume, so all changes made to your shared volume even from inside the container are persistent.
 1. Replicate the folder RoboticsBackend:/RoboticsAcademy inside your local file system. This can be achieved by cloning the repository [RoboticsAcademy][https://github.com/JdeRobot/RoboticsAcademy] and the repository [RoboticsApplicationManager][https://github.com/JdeRobot/RoboticsApplicationManager] inside the folder RoboticsAcademy/src (include the contents directly, not into a subfolder src/RoboticsApplicationManager).
 2. Run ``` yarn run dev ``` if there were any front-end changes
-3. Run the docker image mounting the volume with the instruction ``` docker run --rm -it $(nvidia-smi >/dev/null 2>&1 && echo "--gpus all" || echo "") --device /dev/dri -p 7164:7164 -p 6080:6080 -p 6081:6081 -p 1108:1108 -p 7163:7163 --mount type=bind,source="$(pwd)"/RoboticsAcademy,target=/RoboticsAcademy jderobot/robotics-academy ``` replacing the mount source path to your RoboticsAcademy path.
+3. Run the docker image mounting the volume with the instruction ``` docker run --rm -it $(nvidia-smi >/dev/null 2>&1 && echo "--gpus all" || echo "") --device /dev/dri -p 7164:7164 -p 6080-6090:6080-6090 -p 7163:7163 --mount type=bind,source="$(pwd)"/RoboticsAcademy,target=/RoboticsAcademy jderobot/robotics-academy ``` replacing the mount source path to your RoboticsAcademy path.
 
 [How to set up the developer environment section.]: /docs/InstructionsForDevelopers.md

docs/gpu_acceleration.md

@@ -10,20 +10,20 @@ ROS and Gazebo can be accelerated within RoboticsAcademy thanks to VirtualGL if
 ### Auto
 If the PC has several GPUs, it will select them in order: NVIDIA, Intel, Only CPU.
 ```
-docker run --rm -it $(nvidia-smi >/dev/null 2>&1 && echo "--gpus all" || echo "") --device /dev/dri -p 7164:7164 -p 6080:6080 -p 1108:1108 -p 7163:7163 --link academy_db jderobot/robotics-academy:latest
+docker run --rm -it $(nvidia-smi >/dev/null 2>&1 && echo "--gpus all" || echo "") --device /dev/dri -p 7164:7164 -p 6080-6090:6080-6090 -p 7163:7163 --link academy_db jderobot/robotics-academy:latest
 ```
 
 
 ### Intel
 For Linux machines and Intel GPUs, acceleration can be achieved by simply setting the ```--device``` argument when running the Docker container:
 ```
-docker run --rm -it --device /dev/dri -p 7164:7164 -p 6080:6080 -p 6081:6081 -p 1108:1108 -p 7163:7163 --link academy_db jderobot/robotics-academy:latest
+docker run --rm -it --device /dev/dri -p 7164:7164 -p 6080-6090:6080-6090 -p 7163:7163 --link academy_db jderobot/robotics-academy:latest
 ```
 
 ### NVIDIA
 For NVIDIA GPUs, acceleration can be achieved by [installing the nvidia-container-runtime package](https://docs.docker.com/config/containers/resource_constraints/#gpu), and then running the command above, but adding the ```--gpus all``` flag:
 ```
-docker run --rm -it --gpus all --device /dev/dri -p 7164:7164 -p 6080:6080 -p 6081:6081 -p 1108:1108 -p 7163:7163 --link academy_db jderobot/robotics-academy:latest
+docker run --rm -it --gpus all --device /dev/dri -p 7164:7164 -p 6080-6090:6080-6090 -p 7163:7163 --link academy_db jderobot/robotics-academy:latest
 ```
 
 ## Windows
@@ -33,5 +33,5 @@ For Windows machines, acceleration can be achieved for NVIDIA GPUs if a valid CU
 
 Once these requirements are ready, you should be able to run Robotics Academy with GPU acceleration as follows:
 ```
-docker run --rm -it --gpus all -v /usr/lib/wsl:/usr/lib/wsl -e LD_LIBRARY_PATH=/usr/lib/wsl/lib --device /dev/dri -p 6080:6080 -p 6081:6081 -p 1108:1108 -p 7163:7163 -p 7164:7164 --link academy_db jderobot/robotics-academy:latest
+docker run --rm -it --gpus all -v /usr/lib/wsl:/usr/lib/wsl -e LD_LIBRARY_PATH=/usr/lib/wsl/lib --device /dev/dri -p 6080-6090:6080-6090 -p 7163:7163 -p 7164:7164 --link academy_db jderobot/robotics-academy:latest
 ```

docs/requirements.txt

@@ -1,6 +1,6 @@
 setuptools==57.5.0
 
-django==4.2.24
+django==4.2.25
 channels==1.1.5
 django-extensions==2.2.5
 mysqlclient==1.3.14

exercises/migrations/0001_initial.py

@@ -76,7 +76,7 @@ class Migration(migrations.Migration):
                             ("none", "None"),
                             ("gazebo", "Gazebo"),
                             ("gz", "Gazebo Harmonic"),
-                            ("physical", "Physical"),
+                            ("o3de", "O3de"),
                         ],
                         default="none",
                         max_length=50,

exercises/models.py

@@ -17,7 +17,7 @@
     ("none", "None"),
     ("gazebo", "Gazebo"),
     ("gz", "Gazebo Harmonic"),
-    ("physical", "Physical"),
+    ("o3de", "O3de"),
 )
 
 RosVersion = (("ROS", "ROS"), ("ROS2", "ROS2"))

exercises/static/exercises/3d_reconstruction/react-components/WebGUI.js

@@ -2,6 +2,7 @@ import { useState, useEffect } from "react";
 import { events, states } from "jderobot-commsmanager";
 import { useExercise } from "Contexts/ExerciseContext";
 import WebGUI3D from "Components/exercise/WebGUI3D";
+import WebGUIContainer from "Components/exercise/WebGUIContainer";
 import "./css/Reconstruction3DRR.css";
 import { draw, reset_all } from "./helpers/helperRecontruction";
 
@@ -53,14 +54,7 @@ const WebGUI = () => {
   }, [manager]);
 
   return (
-    <div
-      style={{
-        display: "flex",
-        height: "100%",
-        width: "100%",
-        position: "relative",
-      }}
-    >
+    <WebGUIContainer>
       <WebGUI3D
         toPaint={pointsToPaint}
         reset={reset}
@@ -69,7 +63,7 @@ const WebGUI = () => {
         style={{ height: "100%", width: "33%" }}
       />
       <canvas id="gui_canvas" style={{ width: "67%" }}></canvas>
-    </div>
+    </WebGUIContainer>
   );
 };