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    Coding Guidelines

    -

    Git

    +

    Coding Guidelines

    +

    Git

    • Don't use capital letters in branch names, see https://stackoverflow.com/a/38494084/3866740
    • We use a four-eye principle for merging. Create a merge request in gitlab and ask another developer to approve the changes.
      • @@ -1103,7 +1150,7 @@

      Git

    • Ideally a branch addresses on feature or bug. This makes it easier to test and review.
    • The merge request should explain the changes and link to an issue where applicable.
    -

    Python

    +

    Python

    • In general use python version 3.6.9
    • comply with the PEP8 standard
      • @@ -1114,20 +1161,20 @@

      Python

    Some code guidelines can be automatically enforced with an .editorconfig file (https://editorconfig.org/) if the used editor or IDE supports this.

    -

    Encoding

    +

    Encoding

    The following command lists all *.cpp-files with non trivial encoding, i.e., they might contain some special characters: 

    /bin/find . -type f -name *.cpp -exec file --mime-encoding {} \; | grep -v us-ascii
    All files should be in utf-8 encoding but should only use ascii characters. -

    Tabs vs. Spaces

    +

    Tabs vs. Spaces

    No tab characters should be used. Instead we should use always 4 spaces.

    -

    Brace Formatting

    +

    Brace Formatting

    In C++ braces should always be like that:

    int myFunction(const Vector2d& v) {
   return 2;
 }
    -

    Module Conventions

    +

    Module Conventions

    • parameter Class should be named Parameter
    • the object of the parameter class should be called params
      • @@ -1219,7 +1266,7 @@

      Module Conventions

      - + diff --git a/guides/deeplearning/deep_learning_annotation/index.html b/guides/deeplearning/deep_learning_annotation/index.html old mode 100644 new mode 100755 index f4ce17b..9c72866 --- a/guides/deeplearning/deep_learning_annotation/index.html +++ b/guides/deeplearning/deep_learning_annotation/index.html @@ -698,9 +698,35 @@ + +
    • + + + + + + + + + + +
      • + + + +
    • - - Handling the Robot + + Working with logfiles + +
      • + + + + + + + + + +
    • + + Sound
      • @@ -1147,7 +1194,7 @@ -

      Data Prep for Deep Learning

      +

      Data Prep for Deep Learning

      We use Labelstudio https://labelstud.io/ for all annotation tasks. It's deployed to our k8s cluster. Our instance is available at https://ls.berlinunited-cloud.de/. For more information about the deployment part see the k8s docs.

      We largely automated the data ingestion process from the point of uploading the data to our server.

      @@ -1155,7 +1202,7 @@

      finds new data in the log folder -> adds data to a postgres db -> if images are not already extracted from log, extract them -> add information about image location to db -> if images not already present in labelstudio add them there -> if images not annotated, run annotation model.

      In the following sections this process is described in more detail. Please note that you don't have to run the automation. You can always do everything manually. But for reproducibility this is not advised. The code for the whole data prep process is in https://scm.cms.hu-berlin.de/berlinunited/projects/log-crawler

      -

      Logfolder Structure

      +

      Logfolder Structure

      Internally we have a file server that you can access via any gruenau server. The logs are located at /vol/repl261-vol4/naoth/logs. Externally this folder is accessable via logs.naoth.de. Please see the page about infrastructure for more information.

      logs/
     2015-07-17_RC15/
@@ -1213,7 +1260,7 @@ 
      Logfolder Structure
      
             game_logs/
                 1_91_Nao0379-after-failure/
      -

      Data sources

      +

      Data sources

      Currently we have two main source where image data for deep learning can come from: - log files created on the nao robot - gopro footage from games

      @@ -1225,7 +1272,7 @@

      Data sources

      Note

      TODO: explain the log folder structure somewhere

      -

      CVAT Labeltool

      +

      CVAT Labeltool

      Our CVAT instance can be accessed via https://ball.informatik.hu-berlin.de. The setup of our instance is described in CVAT Setup.

      Everyone can register, but to see the tasks you have to be manually added to a group with the appropriate permissons. @@ -1241,7 +1288,7 @@

      CVAT Labeltool

      To automate many commons tasks python scripts were created in the naoth-deeplearning repo. The most relevant is the function for creating a task. For this you need the name of the task and the path on the server to the zip containing the images. A more detailed documentation can be found in the naoth-deeplearning repo.

      -

      Label Rules

      +

      Label Rules

      • You should not modify tasks that are assigned to someone else
      • You should not modify tasks that are marked completed
        • @@ -1260,7 +1307,7 @@

        Label Rules

        Note

        TODO: Update the rules when circle annotation is possible. Ellipses are not useful for us right now.

      -

      Rules for GoPro Tasks

      +

      Rules for GoPro Tasks

      Starting in 2018 we tried to record every SPL game with a camera outside of the field. The videos can be found at logs.naoth.de

      You should not import the Gopro videos directly. Instead the functions inside the NaoTH-Deeplearning repo for everything.

      @@ -1268,13 +1315,13 @@

      Rules for GoPro Tasks

      TODO: what should the labels be??? TODO: mention that labels should be done in projects not tasks

      -

      Rules for Log Tasks

      +

      Rules for Log Tasks

      Note

      TODO: write down the annotations that dortmund does. its important to know where the robot is blurred, fully in the picture etc.

      -

      Auto Annotation for users

      +

      Auto Annotation for users

      You can see a list of available models by clicking Models View on the top: models_view

      By clicking on supported labels you can see a list of labels this model can annotate. To create new models see the documentation at @@ -1287,9 +1334,9 @@

      Auto Annotation for users

      Those models are not able to do proper annotations (e.g. blurrendness) for bounding boxes yet. Those still have to be done manually.

    -

    Training a model

    +

    Training a model

    TODO

    -

    Deploying a model

    +

    Deploying a model

    TODO

    @@ -1369,7 +1416,7 @@

    Deploying a model

    - + diff --git a/guides/deeplearning/deep_learning_deployment/index.html b/guides/deeplearning/deep_learning_deployment/index.html old mode 100644 new mode 100755 index 3adf73c..b1a40b9 --- a/guides/deeplearning/deep_learning_deployment/index.html +++ b/guides/deeplearning/deep_learning_deployment/index.html @@ -698,9 +698,35 @@ + +
  • + + + + + + + + + + +
    • + + + +
  • - - Handling the Robot + + Working with logfiles + +
    • + + + + + + + + + +
  • + + Sound
    • @@ -1105,14 +1152,14 @@ -

    Deep Learning Deployment Overview

    +

    Deep Learning Deployment Overview

    Note

    TODO: this is very much a WIP. For now just some notes so that we don't forget. We will fix this in the future

    For deployment there are multiple options. The easiest to just use python for inference. However this is probably not what you want in an embedded system.

    -

    Tensorflow inference with python

    +

    Tensorflow inference with python

    The easiest method is just to use python similar to the way we train models.

    Compile Tensorflow for Nao @@ -1140,7 +1187,7 @@

    Tensorflow inference with python

    step and package building again.

    Example inference code and the compiled pip package can be found at ... (TODO: expose the lib folder to the public)

    -

    tensorflow lite on Nao with C++

    +

    tensorflow lite on Nao with C++

    1. Setup ubuntu on the nao as described elsewhere (TODO: insert link)
    2. build the tensorflow library directly on the nao robot:
      3. @@ -1220,7 +1267,7 @@

      tensorflow lite on Nao with C++

      } -

      tensorflow lite micro (tflm) on Nao with C++

      +

      tensorflow lite micro (tflm) on Nao with C++

      Setup ubuntu on the nao as described elsewhere (TODO: insert link) Make sure python3, pip and the pillow package is installed on the Nao 

      # this is needed during tflm compilation
@@ -1233,9 +1280,9 @@ 
      tensorflow lite micro (tflm) o
 make -f tensorflow/lite/micro/tools/make/Makefile microlite
      The compiled lib will be in tflm_src/tensorflow/lite/micro/tools/make/gen/linux_x86_64_default/lib/libtensorflow-microlite.a -

      Create a tflite file

      +

      Create a tflite file

      TODO explain exporter options

      -

      Convert a model for inference with tflite-micro

      +

      Convert a model for inference with tflite-micro

      xxd -i cool_model.tflite > my_model.cc
      this will produce something like this: @@ -1307,7 +1354,7 @@

      Convert a model for inf -DTF_LITE_STATIC_MEMORY

    The last line is especially important. Without it you will get unpredictable behavior using this code. -

    CompiledNN

    +

    CompiledNN

    CompiledNN is a library from B-Human. You can compile it on the Nao itself with:

    git clone https://github.com/bhuman/CompiledNN.git
 cd CompiledNN
@@ -1458,7 +1505,7 @@ 
    CompiledNN
    
     
    
       
    
     
    
-    
+    
     
     
       
diff --git a/guides/deeplearning/training/index.html b/guides/deeplearning/training/index.html
old mode 100644
new mode 100755
index 70ce8d6..c973332
--- a/guides/deeplearning/training/index.html
+++ b/guides/deeplearning/training/index.html
@@ -698,9 +698,35 @@
   
   
   
+    
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    • 
   
@@ -1023,7 +1070,7 @@
                 
 
 
-
    Training Deep Learning Models
    
+
    Training Deep Learning Models
    
 
    We strive to make our work as reproducible as possible. Thus we established the following rules for developing deep learning models within our team:
    
 
      
 
    • when training on shared infrastructure make sure you don't use all the resources
      • 
@@ -1033,7 +1080,7 @@
 
    • datasets used for training should be published to datasets.naoth.de  
      • 
 
    • trained models should be published at models.naoth.de  
      • 
 
    
-
    Train Models inside docker
    
+
    Train Models inside docker
    
 
    
 
    Note
    
 
    TODO: fix user permissions, add restrictions for cpu, how to train headless?
@@ -1133,7 +1180,7 @@ 
    Train Models inside docker
    
     
    
       
    
     
    
-    
+    
     
     
       
diff --git a/guides/git-hooks/index.html b/guides/git-hooks/index.html
old mode 100644
new mode 100755
index 5f8b2bf..8f72cf0
--- a/guides/git-hooks/index.html
+++ b/guides/git-hooks/index.html
@@ -698,9 +698,35 @@
   
   
   
+    
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    • 
   
@@ -977,7 +1024,7 @@
                 
 
 
-
    Git-Hooks
    
+
    Git-Hooks
    
 
    
 
    Note
    
 
    TODO: explain how to set them up on windows with cygwin.
    
@@ -1120,7 +1167,7 @@
     
    
       
    
     
    
-    
+    
     
     
       
diff --git a/guides/keyframe_motions/index.html b/guides/keyframe_motions/index.html
old mode 100644
new mode 100755
index 25363be..a7620c5
--- a/guides/keyframe_motions/index.html
+++ b/guides/keyframe_motions/index.html
@@ -698,9 +698,35 @@
   
   
   
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    • 
   
@@ -1049,11 +1096,11 @@
                 
 
 
-
    Keyframe Motions
    
+
    Keyframe Motions
    
 
    We use use keyframes for some motions. Most notably the get up motions. To describe the keyframes we have a custom format
 which is described in the next section.
    
 
    TODO: alles muss hier überarbeitet werden
    
-
    Structure of the motion files (.mef)
    
+
    Structure of the motion files (.mef)
    
 
    nao
 
 # Kommentar
@@ -1081,9 +1128,9 @@ 
    Structure of the motion files (.mef)<
 
    Dann kommen die 24 Gelenkwerte als Doublewerte ....außer der Wert wurde nicht,
 dann wird ein Stern gesetzt. Bei Stern wird die Varible "valid" auf false gesetzt,
 sonst auf true
    
-
    Keyframe Motion Editor
    
+
    Keyframe Motion Editor
    
 
    TODO
    
-
    How to develop key frame motions
    
+
    How to develop key frame motions
    
 
    TODO
    
 
               
@@ -1163,7 +1210,7 @@ 
    How to develop key frame motions
    
     
    
       
    
     
    
-    
+    
     
     
       
diff --git a/guides/logging/index.html b/guides/logging/index.html
old mode 100644
new mode 100755
index 7785736..ec5bbb0
--- a/guides/logging/index.html
+++ b/guides/logging/index.html
@@ -698,9 +698,35 @@
   
   
   
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@@ -1049,16 +1096,16 @@
                 
 
 
-
    Logging
    
+
    Logging
    
 
    In the context of Robocup we collect different kind of logs.
    
-
    Game and Image Logs
    
+
    Game and Image Logs
    
 
    während spielen aufgenommen in dateien im /tmp Ordner
 sind nach neustart von roboter weg
 was wie oft geloggt wird ist im GameLogger Module festgelegt. 
    
 
    Nach jedem Spiel sollte ein LogStick in den Roboter gesteckt werden um die Logs einzusammeln. Diese sollen anschließend zu
 logs.naoth.de hochgeladen werden
    
-
    Custom Logs
    
-
    TeamComm Logs
    
+
    Custom Logs
    
+
    TeamComm Logs
    
 
    you can control the logging folder via the logDirPath parameter from the gamelogger module. The default is "/tmp". 
 This parameter applies to the game.log and image log that are written automatically. This does not apply for manually recorded logfiles with robotcontrol.
    
 
    TODO add image from robotcontrol here
    
@@ -1140,7 +1187,7 @@ 
    TeamComm Logs
    
     
    
       
    
     
    
-    
+    
     
     
       
diff --git a/guides/obstacle_detection/index.html b/guides/obstacle_detection/index.html
old mode 100644
new mode 100755
index 08dee86..6f51199
--- a/guides/obstacle_detection/index.html
+++ b/guides/obstacle_detection/index.html
@@ -698,9 +698,35 @@
   
   
   
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@@ -1063,28 +1110,28 @@
                 
 
 
-
    Obstacle Detection and Pathplanning
    
+
    Obstacle Detection and Pathplanning
    
 
    Pathplanning for a single robot is easy as long as there are no obstacles to move around. In our case pathplanning can
 be challenging due to the dynamic nature of the robots on the field. There are multiple ways to detect obstacles. First
 we can detect if we are pushed or not able to move as expected. Then we can detect obstacles from further away with the 
 ultrasound sensors or by detecting robots and other obstacles elements in the image. We can also take the broadcasted 
 positions of our teammates into account.
    
-
    Collision Detection
    
+
    Collision Detection
    
 
    We have three different methods to detect collisions. The ArmCollisionDetector module detects wether the arm movements
 are blocked. In this case we try to move our arms back to reduce the width of the robot.
    
 
    The BumperCollisionDetector module detects if the bumper on the feet are pressed for a period of time. This happens 
 when the robots walks into a fallen robot or agains a goalpost.
    
 
    The BodyCollisionDetector module detects wether the robot was pushed. 
    
 
    Collision Detection
    
-
    Obstacle Detection
    
+
    Obstacle Detection
    
 
      
 
    • Ultrasound
      • 
 
    • NoGreenDetection
      • 
 
    • Robot Detection via Deep Learning
      • 
 
    
 
    Obstacle Detection
    
-
    Modeling the obstacles
    
-
    Pathplanning
    
+
    Modeling the obstacles
    
+
    Pathplanning
    
 
    TODO: steffen
    
 
               
@@ -1164,7 +1211,7 @@ 
    Pathplanning
    
     
    
       
    
     
    
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diff --git a/guides/onboarding/index.html b/guides/onboarding/index.html
old mode 100644
new mode 100755
index e690728..5855b4d
--- a/guides/onboarding/index.html
+++ b/guides/onboarding/index.html
@@ -739,9 +739,35 @@
   
   
   
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@@ -1021,7 +1068,7 @@
                 
 
 
-
    Joining the team
    
+
    Joining the team
    
 
    We are always happy to welcome new members in our team. You don't need to be a HU student. For HU students there is also
 the possibility of doing your diploma/master-thesis or student research project (“Studienarbeit”) at our working group.
    
 
    If you are interested to join please contact us via email or come directly to our lab. You can find our contact infos
@@ -1031,7 +1078,7 @@
 accounts of want to use another one we have to manually approve you. In this case send us an email.
 We have weekly zoom meetings. If you want to participate ask us for the link via email or slack. 
 The day and time changes every semester. Please have a look at the website for the current dates.
    
-
    Onboarding
    
+
    Onboarding
    
 
    The development happens on the universities gitlab Server (https://scm.cms.hu-berlin.de/) You can login with your CMS
 credentials or register as an external user. To join our gitlab group send us your gitlab user name.
    
 
    A lot of our code is also publicly available at https://github.com/BerlinUnited.
@@ -1114,7 +1161,7 @@ 
    Onboarding
    
     
    
       
    
     
    
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diff --git a/guides/other_teams/index.html b/guides/other_teams/index.html
old mode 100644
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@@ -1075,10 +1122,10 @@
                 
 
 
-
    Setup code from other teams
    
+
    Setup code from other teams
    
 
    There are a couple of code releases that might be interesting to setup for experiments. This is a guide on how to do this.
    
 
    TODO: explain the multiple repositories and give an overview on how they are connected
    
-
    HTWK Motion Library
    
+
    HTWK Motion Library
    
 
    Since we don't currently have crosscompiler that supports C++17 we have to setup the ubuntu image on the nao and compile the library there. A guide on how to setup the ubuntu image can be found at TODO.
    
 
    On the Nao:
    
 - install git, cmake, g++, unzip
    
@@ -1089,11 +1136,11 @@ 
    HTWK Motion Library
    
 - cmake ../
 - cmake --build .
    
 
    you end up with a libWalkingEngine.a
    
-
    BHuman Coderelease 2020
    
+
    BHuman Coderelease 2020
    
 
    TODO
    
-
    Compiling Code for the robot via other means
    
+
    Compiling Code for the robot via other means
    
 
    Sometimes you want/need to compile something for the robot and not incorporate that into our premake builds. Usually those things are libraries that come with the cmake build system.
    
-
    Minimal cmake example
    
+
    Minimal cmake example
    
 
    TODO: what about sysroot?
 You can compile this helloworld.cpp
 
    #include<iostream>
@@ -1197,7 +1244,7 @@ 
    Minimal cmake example
    
     
    
       
    
     
    
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diff --git a/guides/robot_handling/index.html b/guides/robot_handling/index.html
old mode 100644
new mode 100755
index a63250d..04c217f
--- a/guides/robot_handling/index.html
+++ b/guides/robot_handling/index.html
@@ -697,29 +697,29 @@
               
   
   
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         Logging
@@ -1077,17 +1126,17 @@
                 
 
 
-
    Handling the Robot
    
+
    Handling the Robot
    
 
    Explain the handling of the robot in various scenarios.
    
-
    How to carry the robot
    
+
    How to carry the robot
    
 
    TODO
    
-
    Buttons and LED's
    
+
    Buttons and LED's
    
 
    TODO: explain LED's and buttons  
    
-
    How to inspect for hardware damage
    
+
    How to inspect for hardware damage
    
 
    TODO
    
-
    USB drive stuff
    
+
    USB drive stuff
    
 
    TODO: wo soll das hin?
    
-
    Camera Matrix Calibration
    
+
    Camera Matrix Calibration
    
 
    TODO: auto calibration
 TODO: manuall calibration
  - how to set start conditions
@@ -1170,7 +1219,7 @@ 
    Camera Matrix Calibration
    
     
    
       
    
     
    
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diff --git a/guides/robots/img/output.png b/guides/robots/img/output.png
new file mode 100755
index 0000000000000000000000000000000000000000..8de25c92692e099837cdd4b3efc7342e1e217e5f
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z;CCCmPZqBMd*H`zo){Qg0>j*!;QaO}!{1AICh`4&mCpf$P
zgH9CUoiZMhH-
+
+  
+    
+      
+      
+      
+      
+      
+      
+      
+    
+    
+      
+        Known Issues with Robots - Berlin United - Documentation
+      
+    
+    
+      
+      
+        
+        
+        
+      
+      
+    
+    
+    
+      
+        
+        
+        
+        
+        
+      
+    
+    
+      
+    
+    
+    
+      
+
+    
+    
+  
+  
+  
+    
+    
+      
+    
+    
+    
+    
+    
+  
+    
+    
+      
+    
+    
+    
+    
+    
+    
      
+      
+    
      
+    
+    
+      
+
+  
+
+
      
+  
+  
+    
+      
+
+    
+  
+
      
+    
+    
      
+      
+      
+        
+      
+      
      
+        
      
+          
+            
+              
+              
      
+                
      
+                  
      
+                    
+
+  
+
+
+
+                  
      
+                
      
+              
      
+            
+            
+              
+              
      
+                
      
+                  
      
+                    
+
+
+                  
      
+                
      
+              
      
+            
+          
+          
      
+            
      
+              
+                
+
+
+
      Known Issues with Robots
      
+
      LOLA: Failure Writing Data
      
+
      This error seems to accur sometimes after the robot comes back from being repaired when use a custom OS on the NAO.
      
+
      Symptoms
      
+
      Cognition is started, LEDs are working, but the robot is unable to move the joints.
      
+
      Analysis
      
+
        
+
      1. login through SSH
        2. 
+
      2. run journalctl
        3. 
+
      3. the output should be showing following repeating error messages 
        4. 
+
      
+
      terminal output
      
+
      Reason
      
+
      The reason seems to be a version-mismatch between the firmware of the joints and the communication protocol of the LOLA in the custom OS.
      
+
      Solution
      
+
        
+
      1. flash an original Aldebaran OpenNao OS
        2. 
+
      2. flash the custom OS again
        3. 
+
      
+
+              
+            
      
+            
+          
      
+        
      
+        
+      
      
+      
+        
+
+
+
+      
+    
      
+    
      
+      
      
+    
      
+    
+    
+    
+      
+      
+        
+      
+        
+      
+        
+      
+    
+  
+
\ No newline at end of file
diff --git a/guides/setup/nao_config/index.html b/guides/setup/nao_config/index.html
old mode 100644
new mode 100755
index 9bb6724..72bac50
--- a/guides/setup/nao_config/index.html
+++ b/guides/setup/nao_config/index.html
@@ -710,9 +710,35 @@
   
   
   
+    
+    
    1. 
+      
+      
+        
+      
+      
+      
+      
+        
+      
+      
+    
      2. 
+  
+
+            
+          
             
               
   
   
   
     
    2. 
-      
-        Handling the Robot
+      
+        Working with logfiles
+      
+    
      3. 
+  
+
+            
+          
+            
+              
+  
+  
+  
+    
    3. 
+      
+        Sound
       
     
      4. 
   
@@ -977,7 +1024,7 @@
                 
 
 
-
      Configuration Files
      
+
      Configuration Files
      
 
      TODO: describe the logic behind our configuration files
      
 
               
@@ -1057,7 +1104,7 @@
     
      
       
      
     
      
-    
+    
     
     
       
diff --git a/guides/setup/naoth_code_setup/index.html b/guides/setup/naoth_code_setup/index.html
old mode 100644
new mode 100755
index 8525d75..2447f5d
--- a/guides/setup/naoth_code_setup/index.html
+++ b/guides/setup/naoth_code_setup/index.html
@@ -704,40 +704,6 @@
     Clone and build
   
   
-    
-  
 
       
         
    4. 
@@ -803,9 +769,35 @@
   
   
   
+    
+    
    5. 
+      
+      
+        
+      
+      
+      
+      
+        
+      
+      
+    
      6. 
+  
+
+            
+          
             
               
   
   
   
     
    6. 
-      
-        Handling the Robot
+      
+        Working with logfiles
+      
+    
      7. 
+  
+
+            
+          
+            
+              
+  
+  
+  
+    
    7. 
+      
+        Sound
       
     
      8. 
   
@@ -1083,40 +1096,6 @@
     Clone and build
   
   
-    
-  
 
       
         
    8. 
@@ -1147,8 +1126,8 @@
                 
 
 
-
      NaoTH Development Setup
      
-
      Prerequisites
      
+
      NaoTH Development Setup
      
+
      Prerequisites
      
 
      You need to install a bunch of software before being able to develop code for the Nao robot.
      
 
      
 
      
@@ -1255,7 +1234,7 @@ 
      Prerequisites
      
 
      
 
      
 
      
-
      Toolchain Setup
      
+
      Toolchain Setup
      
 
      
 
      
 
      
@@ -1278,7 +1257,7 @@ 
      Toolchain Setup
      
 
      
 
      
 
      
-
      Clone and build
      
+
      Clone and build
      
 
      To work with the project, checkout the git repo:
 
      git clone <url of this repo> <NaoTH-Projekt/Naoth-2020>
 
      
@@ -1293,9 +1272,9 @@ 
      Clone and build
      
 
      
 
      There are two ways to compile the naoth source code for the NAO. First you can target the softbank provided
 image for the NAO or use clang and target the self build Ubuntu image.
      
-
      Softbank Image
      
+
      Softbank Image
      
 
      In order to build the naoth binary go to <NaoTH-Projekt>/Naoth-2020/NaoTHSoccer/Make and run ./compileGame.sh -j 4
      
-
      Clang + Ubuntu Image
      
+
      Clang + Ubuntu Image
      
 
      You need to have LLVM installed as described above in the prerequisites section.
      
 
      To tell premake to use the ubuntu toolchain you need to change two lines <NaoTH-Projekt>/Naoth-2020/NaoTHSoccer/Make/projectconfig.user.lua  
      
 
        
@@ -1314,11 +1293,11 @@ 
        Clang + Ubuntu Image
        
 
        
 
        There are two ways to compile the naoth source code for the NAO. First you can use cygwin and target the softbank provided
 image for the NAO or use clang and target the self build Ubuntu image.
        
-
        Cygwin + Softbank Image
        
+
        Cygwin + Softbank Image
        
 
        You need to have cygwin installed as described above in the prerequisites section.
        
 
        In order to build the naoth binary go to <NaoTH-Projekt>/Naoth-2020/NaoTHSoccer/Make, open the mintty bash console in
 this folder via the context menu and run ./compileGame.sh -j 4
        
-
        Clang + Ubuntu Image
        
+
        Clang + Ubuntu Image
        
 
        You need to have cygwin and LLVM installed as described above in the prerequisites section.
        
 
        To tell premake to use the ubuntu toolchain you need to change two lines <NaoTH-Projekt>/Naoth-2020/NaoTHSoccer/Make/projectconfig.user.lua  
        
 
          
@@ -1333,7 +1312,7 @@ 
          Clang + Ubuntu Image
          
 
        
 
        
 
        For development inside WSL follow the linux instructions.
        
-
        Additional tools for development
        
+
        Additional tools for development
        
 
        We use different tools with our project:
        
 
        XabslEditor
        
 We have a dedicated editor for editing and compiling the robots behavior written in xabsl.
@@ -1358,7 +1337,7 @@ 
        Additional tools for development
        
 
        Python 
        
 For working with logfiles we have a set of python scripts in the utils/py folder. The basic functionality is inside a the naoth python package which you can install with pip:  
        
    - run pip install -e naoth in the <repository>/Utils/py this will install protobuf as well 
        
-
        Known Issues
        
+
        Known Issues
        
 
        Premake5 is not found
        
   - can happen if your PATH is exceeding the 1024 character limit if PATH is set with setup.bat
   - Solution add it manualy to the Path. Note that trying to append to PATH with the setup.bat script can delete the 
@@ -1450,7 +1429,7 @@ 
        Known Issues
        
     
        
       
        
     
        
-    
+    
     
     
       
diff --git a/guides/setup/robot_setup/index.html b/guides/setup/robot_setup/index.html
old mode 100644
new mode 100755
index 60a7fc1..392b75e
--- a/guides/setup/robot_setup/index.html
+++ b/guides/setup/robot_setup/index.html
@@ -782,9 +782,35 @@
   
   
   
+    
+    
      • 
+      
+      
+        
+      
+      
+      
+      
+        
+      
+      
+    
        • 
+  
+
+            
+          
             
               
   
   
   
     
      • 
-      
-        Handling the Robot
+      
+        Working with logfiles
+      
+    
        • 
+  
+
+            
+          
+            
+              
+  
+  
+  
+    
      • 
+      
+        Sound
       
     
        • 
   
@@ -1105,10 +1152,10 @@
                 
 
 
-
        NAO setup and deployment
        
+
        NAO setup and deployment
        
 
        Before you can deploy the NaoTH Code to the robot needs to be set up in a special way. This is a one time setup which 
 replaces the operating system that comes with the robot and additionally sets up some libraries we need on the robot. 
        
-
        Setup Routine for V5 Robots
        
+
        Setup Routine for V5 Robots
        
 
        First we need to set up the operating system with the image provided by softbank robotics. You can download it from 
 here
        
 
        This image must be put onto a usb drive. For this you can use the 
@@ -1124,14 +1171,14 @@ 
        Setup Routine for V5 Robots
        
 
        The next step is to initialize the robot with the required libs. This can be done with NaoSCP and is described
 here
        
 
        After that the compiled robot code can be deployed with NaoSCP as well.
        
-
        Calibrating Joints
        
+
        Calibrating Joints
        
 
        The default software of the robot has an integrated automatic calibration routine for the alignment of the joints.
 This routine is usually executed only at the first start of a new robot.
 During the RoboCup games it can happen (and it does happen) that the joints lose their alignment, 
 which might negatively affect all motions, e.g., the walk become a bit more unstable. 
 The calibration values are stored internally and are preserved even when the OS is replaced.
 The following describes how the automatic calibration routine can be triggered when needed.
        
-
        Trigger the Automatic Calibration Routine
        
+
        Trigger the Automatic Calibration Routine
        
 
          
 
        1. 
 
          prepare a USB stick with NaoOS Image (as described above) with factory reset.
          
@@ -1188,16 +1235,16 @@ 
          Trigger the Automatic Calibra
 
        
 
 
    
-
    Reference
    
+
    Reference
    
 
-
    New Setup Routine (with custom image)
    
+
    New Setup Routine (with custom image)
    
 
    For the NAO V6 robots we will use a custom ubuntu based image. Each robot has to be set up with this image once. For creating 
 the image see the NaoImage repository in gitlab. (Not public). The instructions for building and deploying the image can
 be found there.
    
 
    
-
    Deploy Code to robot
    
+
    Deploy Code to robot
    
 
    TODO: explain network stick
    
 TODO: explain deploy stick
    
 TODO: explain deploy routines?
    
@@ -1279,7 +1326,7 @@ 
    Deploy Code to robot
    
     
    
       
    
     
    
-    
+    
     
     
       
diff --git a/guides/tools/index.html b/guides/tools/index.html
new file mode 100755
index 0000000..e539e4a
--- /dev/null
+++ b/guides/tools/index.html
@@ -0,0 +1,1140 @@
+
+
+
+  
+    
+      
+      
+      
+      
+      
+      
+      
+    
+    
+      
+        Tools - Berlin United - Documentation
+      
+    
+    
+      
+      
+        
+        
+        
+      
+      
+    
+    
+    
+      
+        
+        
+        
+        
+        
+      
+    
+    
+      
+    
+    
+    
+      
+
+    
+    
+  
+  
+  
+    
+    
+      
+    
+    
+    
+    
+    
+  
+    
+    
+      
+    
+    
+    
+    
+    
+    
    
+      
+    
    
+    
+    
+      
+
+  
+
+
    
+  
+  
+    
+      
+
+    
+  
+
    
+    
+    
    
+      
+      
+        
+      
+      
    
+        
    
+          
+            
+              
+              
    
+                
    
+                  
    
+                    
+
+  
+
+
+
+                  
    
+                
    
+              
    
+            
+            
+              
+              
    
+                
    
+                  
    
+                    
+
+
+                  
    
+                
    
+              
    
+            
+          
+          
    
+            
    
+              
+                
+
+
+
    Tools
    
+
    Git / SmartGit
    
+
      
+
    • SmartGit is a git client used by our team. It can be used for free for non-profit and academic projects.
      • 
+
    • https://www.syntevo.com/smartgit/
      • 
+
    • https://www.syntevo.com/register-non-commercial/#academic
      • 
+
    
+
    Visual Studio Code
    
+
    
+
+              
+            
    
+            
+          
    
+        
    
+        
+      
    
+      
+        
+
+
+
+      
+    
    
+    
    
+      
    
+    
    
+    
+    
+    
+      
+      
+        
+      
+        
+      
+        
+      
+    
+  
+
\ No newline at end of file
diff --git a/guides/working-with-logfiles/img/rc_connect.jpg b/guides/working-with-logfiles/img/rc_connect.jpg
old mode 100644
new mode 100755
diff --git a/guides/working-with-logfiles/img/rc_imagedrawings.jpg b/guides/working-with-logfiles/img/rc_imagedrawings.jpg
old mode 100644
new mode 100755
diff --git a/guides/working-with-logfiles/img/rc_imageviewer.jpg b/guides/working-with-logfiles/img/rc_imageviewer.jpg
old mode 100644
new mode 100755
diff --git a/guides/working-with-logfiles/img/rc_loginspector_image_viewer.png b/guides/working-with-logfiles/img/rc_loginspector_image_viewer.png
old mode 100644
new mode 100755
diff --git a/guides/working-with-logfiles/img/rc_loginspector_representations.png b/guides/working-with-logfiles/img/rc_loginspector_representations.png
old mode 100644
new mode 100755
diff --git a/guides/working-with-logfiles/working-with-logfiles/index.html b/guides/working-with-logfiles/working-with-logfiles/index.html
old mode 100644
new mode 100755
index ccfc231..f560de3
--- a/guides/working-with-logfiles/working-with-logfiles/index.html
+++ b/guides/working-with-logfiles/working-with-logfiles/index.html
@@ -697,21 +697,37 @@
               
   
   
-    
-  
   
-    
  • 
+    
+    
  • 
       
-      
       
+        
       
-        
       
       
-      
-        Working with logfiles
-      
       
+        
+      
+      
+    
    • 
+  
+
+            
+          
+            
+              
+  
+  
+    
+  
+  
+    
  • 
+      
+      
+      
+      
+        
+      
+      
+      
+        Working with logfiles
+      
+      
+    
    • 
+  
+
+            
+          
             
               
   
   
   
     
  • 
-      
-        Handling the Robot
+      
+        Sound
       
     
    • 
   
@@ -978,7 +1025,7 @@
 
    Prerequisites:
    
 - You should be able to compile the Commons, NaothSoccer and LogSimulator projects on your system
    
 - You can run RobotControl  
    
-
    1 Get access to logfiles
    
+
    1 Get access to logfiles
    
 
    There are two ways to access the folder with the logfiles and videos of our games and tests. 
    
 
      
 
    1. You can get read-only access to the logs (without the need of the ssh access) at
      2. 
@@ -1000,14 +1047,14 @@ 
      1 Get access to logfiles
      
 
    
 
    For Linux use ssh-keygen and on Windows use cygwin's ssh-keygen. 
    Send your public key to a teammember who can add your key.
    
-
    2 Work with logfiles in RobotControl
    
-
    Run a logfile in the LogSimulator
    
+
    2 Work with logfiles in RobotControl
    
+
    Run a logfile in the LogSimulator
    
 
    For this tutorial use the following file  
    
 
 
    Download a logfile and start the LogSimulator in Debug with the name of the logfile as argument. In the Console window you can press p to play the logfile and l to loop the logfile. More Controls for the LogSimulator are displayed in the console window.
    
-
    Start RobotControl
    
+
    Start RobotControl
    
 
    A detailed description of the RobotControl can be found in the Teamreport.
 There are multiple ways to start RobotControl:
    
 
      
@@ -1033,8 +1080,8 @@ 
      Start RobotControl
      
 
      In the Finder, go to naoth-2020/RobotControl and double-click the granlew script. Same for the robotcontrol script located in naoth-2020/RobotControl/dist.
      
 
 
    
-
    View Representations in RobotControl
    
-
    Logfile Inspector
    
+
    View Representations in RobotControl
    
+
    Logfile Inspector
    
 
    Depending on what you want to do with the logfiles, you can view them in different ways. If you want to check if the logfiles contain any data and quickly look through it, you can use the Logfile Inspector, which gives you the possibility to view the data (e.g. images, sensor values) in RobotControl:
    
 
      
 
    1. 
@@ -1064,7 +1111,7 @@ 
      Logfile Inspector
      
 Screenshot_2021-03-19_at_17.34.12
      
 
      2. 
 
    
-
    LogSimulator
    
+
    LogSimulator
    
 
    If you want to run robot code on the logfile, you should create a robot instance by starting the LogSimulator in the terminal, playing the logfile, and connecting RobotControl to your localhost. In this way the logfile is basically acting like a real robot, which you can observe and program via RobotControl.
    
 
      
 
    1. 
@@ -1095,8 +1142,8 @@ 
      LogSimulator
      
 
    
 
    In the image above the DebugRequests for showing the green pixels are enabled 
    (on the left). On the right side of the image the ParameterTreePanel is shown. 
    
-
    3 Work with logfiles in python
    
-
    3.1 Python Ecosystem
    
+
    3 Work with logfiles in python
    
+
    3.1 Python Ecosystem
    
 
    All python ecosystem is located in the directory 
    
 
    <naoth-2020 repository path>/Utils/py
 
    
@@ -1118,12 +1165,12 @@ 
    3.1 Python Ecosystem

    NOTE: The python messages are saves in Utils/py/naoth/naoth/pb. If you don't have the up-to-date messages, the logs can't be parsed.

    -

    3.2 Example Logfile

    +

    3.2 Example Logfile

    For our examples we use the following logfile 

    https://logs.naoth.de/2019-07-02_RC19/2019-07-04_11-00-00_Berlin%20United_vs_NaoDevils_half1/game_logs/3_93_Nao0212_190704-1112/game.log
    We assume, that the logfile is named game.log and is placed in the same directory as your scripts. -

    3.3 Logfiles structure

    +

    3.3 Logfiles structure

    A logfile consists of frames and each frame contains representations, which hold various data recorded from the robot. Such data can be Images, Ball detections etc. Each Frame has a number increasing from 0. The representations @@ -1140,7 +1187,7 @@

    3.3 Logfiles structure

    | +--... ...
    -

    3.4 Reading a logfile

    +

    3.4 Reading a logfile

    To read and parse a logfile we use the naoth.log.Reader class. More Information can be found in the file

    py/naoth/naoth/log/_experimental_reader.py
    @@ -1163,7 +1210,7 @@

    3.4 Reading a logfile

    Representations: dict_keys(['BehaviorStateComplete', 'RobotInfo', 'BehaviorStateSparse', 'OdometryData', 'CameraMatrix', 'CameraMatrixTop', 'GoalPercept', 'GoalPerceptTop', 'MultiBallPercept', 'BallModel', 'FieldPercept', 'FieldPerceptTop', 'ScanLineEdgelPercept', 'ScanLineEdgelPerceptTop', 'ShortLinePercept', 'RansacLinePercept', 'RansacCirclePercept2018', 'TeamMessage', 'FrameInfo']) -

    3.5 Reading representations

    +

    3.5 Reading representations

    We extend the above example to read the content of an important Representation FrameInfo. FrameInfo contains the timestamp and the number of the frame in the actual execution cycle of the robot. (Note: this frame number doen not correspond to the number of the frame in the logfile).

    from naoth.log import Reader
 
@@ -1182,7 +1229,7 @@ 
    3.5 Reading representations
    
 frameNumber: 1
 time: 1417
    -

    3.6 Other examples

    +

    3.6 Other examples

    Further more complex examples can be found in 

    Utils/py/log_parser_examples
    @@ -1264,7 +1311,7 @@

    3.6 Other examples

    - + diff --git a/guides/working-with-sound/working-with-sound/index.html b/guides/working-with-sound/working-with-sound/index.html old mode 100644 new mode 100755 index abc3265..310aace --- a/guides/working-with-sound/working-with-sound/index.html +++ b/guides/working-with-sound/working-with-sound/index.html @@ -698,6 +698,67 @@ + +
  • + + + + + + + + + + +
    • + + + + + + + + +
  • Working with logfiles @@ -828,20 +889,6 @@ -
  • - - Handling the Robot - -
    • - - - - - - - - -
  • Logging @@ -1143,29 +1190,29 @@ -

    Sound

    -

    Tools

    -

    Audacity

    +

    Sound

    +

    Tools

    +

    Audacity

    A tool for working with audio files

    -

    Pyroomacoustics

    +

    Pyroomacoustics

    A software package aimed at the rapid development and testing of audio array processing algorithms. Can be used to simulate the behavior of sound in space.

    -

    Sound in Logfiles

    +

    Sound in Logfiles

    Logfies recorded by the robots during the game can be found here: * https://logs.naoth.de/

    -

    Code

    +

    Code

    Audio data is recorded in the Logfiles in blocks. The format of the block can be found in the message AudioData.proto and the corresponding representation in AudioData.h.

    • https://github.com/BerlinUnited/NaoTH/blob/develop/Framework/Commons/Messages/AudioData.proto
      • @@ -1188,11 +1235,11 @@

      Code

      -

      Recording with PulseAudio

      -

      Recording RAW audio data

      +

      Recording with PulseAudio

      +

      Recording RAW audio data

          parecord -r --raw --format=s16le --rate=8000 --channel-map=rear-left,rear-right,front-left,front-right --channels=4 > test.raw
      -

      References

      +

      References

      • Akustische Ortung im Roboterfußball, Jakob Dübel, Masterthesis (German).
        • Code (Internal): https://scm.cms.hu-berlin.de/berlinunited/papers/2020-jakob-duebel-masterarbeit
          • @@ -1285,7 +1332,7 @@

          References

          - + diff --git a/img-src/ScanLineEdgelDetector.psd b/img-src/ScanLineEdgelDetector.psd old mode 100644 new mode 100755 diff --git a/img-src/action_selection.psd b/img-src/action_selection.psd old mode 100644 new mode 100755 diff --git a/img-src/ball_examples.psd b/img-src/ball_examples.psd old mode 100644 new mode 100755 diff --git a/img-src/cameramatrix_calibration.psd b/img-src/cameramatrix_calibration.psd old mode 100644 new mode 100755 diff --git a/img-src/collision_detection.ai b/img-src/collision_detection.ai old mode 100644 new mode 100755 diff --git a/img-src/deep_learning/cvat_projects.psd b/img-src/deep_learning/cvat_projects.psd old mode 100644 new mode 100755 diff --git a/img-src/detected_ball.psd b/img-src/detected_ball.psd old mode 100644 new mode 100755 diff --git a/img-src/field_detector.psd b/img-src/field_detector.psd old mode 100644 new mode 100755 diff --git a/img-src/goal_detector.psd b/img-src/goal_detector.psd old mode 100644 new mode 100755 diff --git a/img-src/goal_feature_detector.psd b/img-src/goal_feature_detector.psd old mode 100644 new mode 100755 diff --git a/img-src/goal_modeling.psd b/img-src/goal_modeling.psd old mode 100644 new mode 100755 diff --git a/img-src/green_detection.psd b/img-src/green_detection.psd old mode 100644 new mode 100755 diff --git a/img-src/green_histogram.psd b/img-src/green_histogram.psd old mode 100644 new mode 100755 diff --git a/img-src/key_points.psd b/img-src/key_points.psd old mode 100644 new mode 100755 diff --git a/img-src/line_graph_provider.psd b/img-src/line_graph_provider.psd old mode 100644 new mode 100755 diff --git a/img-src/local_maxima_algorithm.tex b/img-src/local_maxima_algorithm.tex old mode 100644 new mode 100755 index f2b371e..4021616 --- a/img-src/local_maxima_algorithm.tex +++ b/img-src/local_maxima_algorithm.tex @@ -1,31 +1,31 @@ -\documentclass{standalone}% For the example only, any class will do - -\usepackage[ruled,vlined]{algorithm2e} - - -\begin{document} -\begin{algorithm}[H] - \caption{Find local maxima.} - \SetAlgoLined - \KwData{Set of possible key points $A$} - \KwResult{List of locally maximal key points $K$} - - $K\gets \emptyset$\; - \For{$p \in A$} { - ${\tt insert}\gets true$\; - \For{$q \in K$} { - \If{ overlaps(p,q) }{ - \eIf{$V(p) > V(q)$}{ - $K\gets K\setminus\{q\}$\; - }{ - ${\tt insert}\gets false$\; - } - } - } - - \If{{\tt insert}}{ - $K\gets K\cup\{p\}$\; - } - } -\end{algorithm} +\documentclass{standalone}% For the example only, any class will do + +\usepackage[ruled,vlined]{algorithm2e} + + +\begin{document} +\begin{algorithm}[H] + \caption{Find local maxima.} + \SetAlgoLined + \KwData{Set of possible key points $A$} + \KwResult{List of locally maximal key points $K$} + + $K\gets \emptyset$\; + \For{$p \in A$} { + ${\tt insert}\gets true$\; + \For{$q \in K$} { + \If{ overlaps(p,q) }{ + \eIf{$V(p) > V(q)$}{ + $K\gets K\setminus\{q\}$\; + }{ + ${\tt insert}\gets false$\; + } + } + } + + \If{{\tt insert}}{ + $K\gets K\cup\{p\}$\; + } + } +\end{algorithm} \end{document} \ No newline at end of file diff --git a/img-src/obstacle_detection.ai b/img-src/obstacle_detection.ai old mode 100644 new mode 100755 diff --git a/img-src/old_label_tool.psd b/img-src/old_label_tool.psd old mode 100644 new mode 100755 diff --git a/img-src/patches.psd b/img-src/patches.psd old mode 100644 new mode 100755 diff --git a/img-src/prob_compass.psd b/img-src/prob_compass.psd old mode 100644 new mode 100755 diff --git a/img-src/simulation.psd b/img-src/simulation.psd old mode 100644 new mode 100755 diff --git a/img-src/walking_engine.tex b/img-src/walking_engine.tex old mode 100644 new mode 100755 index 8e582f3..9768f96 --- a/img-src/walking_engine.tex +++ b/img-src/walking_engine.tex @@ -1,102 +1,102 @@ -\documentclass{standalone}% For the example only, any class will do - -\usepackage{tikz} -\usetikzlibrary{positioning}% To get more advances positioning options -\usetikzlibrary{arrows.meta} -\usetikzlibrary{fit} -\usetikzlibrary{calc} -\usetikzlibrary{backgrounds} - - -\begin{document} - \tikzset{% - >={Latex[width=2mm,length=2mm]}, - % Specifications for style of nodes: - base/.style = {rectangle, rounded corners, draw=black, - minimum width=4cm, minimum height=1cm, - text centered, font=\sffamily}, - module/.style = {base, minimum width=2.5cm, fill=green!15, font=\ttfamily}, - sub_module/.style = {base, minimum width=2.5cm, fill=white!15, font=\ttfamily}, - rep/.style = {base, minimum width=2.5cm, fill=blue!15, font=\ttfamily}, - robot/.style = {base, minimum width=2.5cm, fill=gray!15, font=\ttfamily}, - } - \begin{tikzpicture}[node distance=1.5cm, every node/.style={fill=white, font=\sffamily}, align=center] - \node (Cognition) [sub_module, minimum width=8cm] {Cognition}; - \node (inv2) [inner sep=0,minimum size=0,below= of Cognition]{}; % invisible node - \node (MotionRequest) [rep, left = of inv2] {Motion Request}; - \node (MotionStatus) [rep, right = of inv2] {Motion Status}; - \node (FootStepPlanner) [sub_module, below = of MotionRequest] {Foot Step Planner}; - \node (StepBuffer) [rep, right = of FootStepPlanner] {Step Buffer}; - \node (inv1) [inner sep=0,minimum size=0,right= 0cm and 2cm of StepBuffer]{}; % invisible node - \node (ZMPPlanner) [sub_module, below = of StepBuffer] {ZMPPlanner}; - \node (ZMPReferenceBuffer) [rep, below = of ZMPPlanner] {ZMP Reference Buffer}; - \node (ZMPPreviewController) [sub_module, below = of ZMPReferenceBuffer] {ZMP Preview Controller}; - \node (FootTrajectoryGenerator) [sub_module, right = of inv1] {Foot Trajectory Generator}; - \node (HipRotationOffsetModifier) [sub_module, below = of FootTrajectoryGenerator] {Hip Rotation Offset Modifier}; - \node (LiftingFootCompensator) [sub_module, below = of HipRotationOffsetModifier] {Lifting Foot Compensator}; - \node (TargetCoMFeetPose) [rep, below = of ZMPPreviewController] {Target CoM Feet Pose}; - \node (IKOpt) [sub_module, below = of TargetCoMFeetPose] {IK Optimizer Hip}; - \node (TargetHipFeetPose) [rep, below = of IKOpt] {Target Hip Feet Pose}; - \node (TorsoRotationStabilizer) [sub_module, right = of TargetHipFeetPose] {Torso Rotation\\Stabilizer}; - \node (IK) [sub_module, below = of TargetHipFeetPose] {Inverse Kinematics}; - \node (MotorJoints) [rep, below = of IK] {Motor Joints}; - \path let \p1=(IK), \p2=(TorsoRotationStabilizer) in node[sub_module] (FeetStabilizer) at (\x2,\y1) {FeetStabilizer}; - \node (CoMErrors) [rep, left = of FootStepPlanner] {CoM Errors}; - \node (CoMErrorProvider) [sub_module, below = of CoMErrors] {CoM Error Provider}; - \node (CommandPoseBuffer) [rep, left = of TargetCoMFeetPose] {CommandPoseBuffer}; - \path let \p1=(MotorJoints), \p2=(CoMErrorProvider) in node[rep] (KinematicChainSensor) at (\x2,\y1) {Kinematic Chain Sensor}; - \node (AccelerometerData) [rep, below right = of MotorJoints] {Accelerometer\\Data}; - \node (IMUModel) [module, right = of AccelerometerData] {IMU-Model}; - \node (GyrometerData) [rep, right = of IMUModel] {Gyrometer\\Data}; - \path let \p1=(MotorJoints), \p2=(IMUModel) in node[rep] (IMUDataInertialModel) at (\x2,\y1) {IMUData / \\InertialModel}; - \node (Robot) [robot, below = 3.5cm and 0cm of MotorJoints] {Robot}; - - \coordinate [right = of HipRotationOffsetModifier] (join1); - \path (TorsoRotationStabilizer) -- (FeetStabilizer) node[shape=coordinate, midway] (join2) {}; - - \begin{scope}[on background layer] - \node (Motion) [sub_module, inner sep=0.5cm, fit=(GyrometerData) (MotionStatus) (MotionRequest) (KinematicChainSensor)] {}; - \node (Walk2018) [module, inner sep=0.8cm, fit=(join1) (IK) (CoMErrorProvider) (CoMErrors)] {}; - \node [below right, opacity=0, text opacity=1] at (Walk2018.north west) {Walk2018-Module}; - \node [below right, opacity=0, text opacity=1] at (Motion.north west) {Motion}; - \end{scope} - - \draw[->] (MotionRequest.north|-Cognition.south) -- (MotionRequest); - \draw[->] (MotionStatus) -- (MotionStatus.north|-Cognition.south); - \draw[->] (MotionRequest) -- (FootStepPlanner); - \draw[->] (FootStepPlanner) -- (StepBuffer); - \draw[->] (StepBuffer) -- (ZMPPlanner); - \draw[->] (StepBuffer) -- (FootTrajectoryGenerator); - \draw[->] (inv1) |- (HipRotationOffsetModifier); - \draw[->] (inv1) |- (LiftingFootCompensator); - \draw[->] (ZMPPlanner) -- (ZMPReferenceBuffer); - \draw[->] (ZMPReferenceBuffer) -- (ZMPPreviewController); - \draw[->] (ZMPPreviewController) -- (TargetCoMFeetPose); - \draw (FootTrajectoryGenerator) -| (join1); - \draw (HipRotationOffsetModifier) -- (join1); - \draw (LiftingFootCompensator) -| (join1); - \draw[->] (join1) |- (TargetCoMFeetPose); - \draw[->] (TargetCoMFeetPose) -- (IKOpt); - \draw[->] (IKOpt) -- (TargetHipFeetPose); - \draw[->] (TargetHipFeetPose) -- (IK); - \draw[->] (FeetStabilizer) |- (MotorJoints); - \draw[->] (IK) -- (MotorJoints); - \draw[->] (MotorJoints) -- (Robot); - \draw (IMUDataInertialModel) |- (join2); - \draw[->] (join2) -- (TorsoRotationStabilizer); - \draw[->] (join2) -- (FeetStabilizer); - \draw (GyrometerData) |- (join2); - \draw[->] (TorsoRotationStabilizer) -- (TargetHipFeetPose); - \draw[->] (CoMErrors) -- (FootStepPlanner); - \draw[->] (CoMErrorProvider) -- (CoMErrors); - \draw[->] (Robot) -| (AccelerometerData); - \draw[->] (Robot) -| (KinematicChainSensor); - \draw[->] (Robot) -| (GyrometerData); - \draw[->] (KinematicChainSensor) -- (CoMErrorProvider); - \draw[->] (CommandPoseBuffer) |- (CoMErrorProvider); - \draw[->] (TargetCoMFeetPose) -- (CommandPoseBuffer); - \draw[->] (IMUModel) -- (IMUDataInertialModel); - \draw[->] (AccelerometerData) -- (IMUModel); - \draw[->] (GyrometerData) -- (IMUModel); - \end{tikzpicture} +\documentclass{standalone}% For the example only, any class will do + +\usepackage{tikz} +\usetikzlibrary{positioning}% To get more advances positioning options +\usetikzlibrary{arrows.meta} +\usetikzlibrary{fit} +\usetikzlibrary{calc} +\usetikzlibrary{backgrounds} + + +\begin{document} + \tikzset{% + >={Latex[width=2mm,length=2mm]}, + % Specifications for style of nodes: + base/.style = {rectangle, rounded corners, draw=black, + minimum width=4cm, minimum height=1cm, + text centered, font=\sffamily}, + module/.style = {base, minimum width=2.5cm, fill=green!15, font=\ttfamily}, + sub_module/.style = {base, minimum width=2.5cm, fill=white!15, font=\ttfamily}, + rep/.style = {base, minimum width=2.5cm, fill=blue!15, font=\ttfamily}, + robot/.style = {base, minimum width=2.5cm, fill=gray!15, font=\ttfamily}, + } + \begin{tikzpicture}[node distance=1.5cm, every node/.style={fill=white, font=\sffamily}, align=center] + \node (Cognition) [sub_module, minimum width=8cm] {Cognition}; + \node (inv2) [inner sep=0,minimum size=0,below= of Cognition]{}; % invisible node + \node (MotionRequest) [rep, left = of inv2] {Motion Request}; + \node (MotionStatus) [rep, right = of inv2] {Motion Status}; + \node (FootStepPlanner) [sub_module, below = of MotionRequest] {Foot Step Planner}; + \node (StepBuffer) [rep, right = of FootStepPlanner] {Step Buffer}; + \node (inv1) [inner sep=0,minimum size=0,right= 0cm and 2cm of StepBuffer]{}; % invisible node + \node (ZMPPlanner) [sub_module, below = of StepBuffer] {ZMPPlanner}; + \node (ZMPReferenceBuffer) [rep, below = of ZMPPlanner] {ZMP Reference Buffer}; + \node (ZMPPreviewController) [sub_module, below = of ZMPReferenceBuffer] {ZMP Preview Controller}; + \node (FootTrajectoryGenerator) [sub_module, right = of inv1] {Foot Trajectory Generator}; + \node (HipRotationOffsetModifier) [sub_module, below = of FootTrajectoryGenerator] {Hip Rotation Offset Modifier}; + \node (LiftingFootCompensator) [sub_module, below = of HipRotationOffsetModifier] {Lifting Foot Compensator}; + \node (TargetCoMFeetPose) [rep, below = of ZMPPreviewController] {Target CoM Feet Pose}; + \node (IKOpt) [sub_module, below = of TargetCoMFeetPose] {IK Optimizer Hip}; + \node (TargetHipFeetPose) [rep, below = of IKOpt] {Target Hip Feet Pose}; + \node (TorsoRotationStabilizer) [sub_module, right = of TargetHipFeetPose] {Torso Rotation\\Stabilizer}; + \node (IK) [sub_module, below = of TargetHipFeetPose] {Inverse Kinematics}; + \node (MotorJoints) [rep, below = of IK] {Motor Joints}; + \path let \p1=(IK), \p2=(TorsoRotationStabilizer) in node[sub_module] (FeetStabilizer) at (\x2,\y1) {FeetStabilizer}; + \node (CoMErrors) [rep, left = of FootStepPlanner] {CoM Errors}; + \node (CoMErrorProvider) [sub_module, below = of CoMErrors] {CoM Error Provider}; + \node (CommandPoseBuffer) [rep, left = of TargetCoMFeetPose] {CommandPoseBuffer}; + \path let \p1=(MotorJoints), \p2=(CoMErrorProvider) in node[rep] (KinematicChainSensor) at (\x2,\y1) {Kinematic Chain Sensor}; + \node (AccelerometerData) [rep, below right = of MotorJoints] {Accelerometer\\Data}; + \node (IMUModel) [module, right = of AccelerometerData] {IMU-Model}; + \node (GyrometerData) [rep, right = of IMUModel] {Gyrometer\\Data}; + \path let \p1=(MotorJoints), \p2=(IMUModel) in node[rep] (IMUDataInertialModel) at (\x2,\y1) {IMUData / \\InertialModel}; + \node (Robot) [robot, below = 3.5cm and 0cm of MotorJoints] {Robot}; + + \coordinate [right = of HipRotationOffsetModifier] (join1); + \path (TorsoRotationStabilizer) -- (FeetStabilizer) node[shape=coordinate, midway] (join2) {}; + + \begin{scope}[on background layer] + \node (Motion) [sub_module, inner sep=0.5cm, fit=(GyrometerData) (MotionStatus) (MotionRequest) (KinematicChainSensor)] {}; + \node (Walk2018) [module, inner sep=0.8cm, fit=(join1) (IK) (CoMErrorProvider) (CoMErrors)] {}; + \node [below right, opacity=0, text opacity=1] at (Walk2018.north west) {Walk2018-Module}; + \node [below right, opacity=0, text opacity=1] at (Motion.north west) {Motion}; + \end{scope} + + \draw[->] (MotionRequest.north|-Cognition.south) -- (MotionRequest); + \draw[->] (MotionStatus) -- (MotionStatus.north|-Cognition.south); + \draw[->] (MotionRequest) -- (FootStepPlanner); + \draw[->] (FootStepPlanner) -- (StepBuffer); + \draw[->] (StepBuffer) -- (ZMPPlanner); + \draw[->] (StepBuffer) -- (FootTrajectoryGenerator); + \draw[->] (inv1) |- (HipRotationOffsetModifier); + \draw[->] (inv1) |- (LiftingFootCompensator); + \draw[->] (ZMPPlanner) -- (ZMPReferenceBuffer); + \draw[->] (ZMPReferenceBuffer) -- (ZMPPreviewController); + \draw[->] (ZMPPreviewController) -- (TargetCoMFeetPose); + \draw (FootTrajectoryGenerator) -| (join1); + \draw (HipRotationOffsetModifier) -- (join1); + \draw (LiftingFootCompensator) -| (join1); + \draw[->] (join1) |- (TargetCoMFeetPose); + \draw[->] (TargetCoMFeetPose) -- (IKOpt); + \draw[->] (IKOpt) -- (TargetHipFeetPose); + \draw[->] (TargetHipFeetPose) -- (IK); + \draw[->] (FeetStabilizer) |- (MotorJoints); + \draw[->] (IK) -- (MotorJoints); + \draw[->] (MotorJoints) -- (Robot); + \draw (IMUDataInertialModel) |- (join2); + \draw[->] (join2) -- (TorsoRotationStabilizer); + \draw[->] (join2) -- (FeetStabilizer); + \draw (GyrometerData) |- (join2); + \draw[->] (TorsoRotationStabilizer) -- (TargetHipFeetPose); + \draw[->] (CoMErrors) -- (FootStepPlanner); + \draw[->] (CoMErrorProvider) -- (CoMErrors); + \draw[->] (Robot) -| (AccelerometerData); + \draw[->] (Robot) -| (KinematicChainSensor); + \draw[->] (Robot) -| (GyrometerData); + \draw[->] (KinematicChainSensor) -- (CoMErrorProvider); + \draw[->] (CommandPoseBuffer) |- (CoMErrorProvider); + \draw[->] (TargetCoMFeetPose) -- (CommandPoseBuffer); + \draw[->] (IMUModel) -- (IMUDataInertialModel); + \draw[->] (AccelerometerData) -- (IMUModel); + \draw[->] (GyrometerData) -- (IMUModel); + \end{tikzpicture} \end{document} \ No newline at end of file diff --git a/img-src/xabsl_editor.psd b/img-src/xabsl_editor.psd old mode 100644 new mode 100755 diff --git a/img/NaoSCP.png b/img/NaoSCP.png old mode 100644 new mode 100755 diff --git a/img/ScanLineEdgelDetector.png b/img/ScanLineEdgelDetector.png old mode 100644 new mode 100755 diff --git a/img/action_selection.png b/img/action_selection.png old mode 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b/img/kickdist.png old mode 100644 new mode 100755 diff --git a/img/labeling_screenshot.png b/img/labeling_screenshot.png old mode 100644 new mode 100755 diff --git a/img/line_graph_provider.png b/img/line_graph_provider.png old mode 100644 new mode 100755 diff --git a/img/local_maxima_algorithm.png b/img/local_maxima_algorithm.png old mode 100644 new mode 100755 diff --git a/img/modules.png b/img/modules.png old mode 100644 new mode 100755 diff --git a/img/naoscp/naoscp_start.png b/img/naoscp/naoscp_start.png old mode 100644 new mode 100755 diff --git a/img/naoth_setup/robot_poses.png b/img/naoth_setup/robot_poses.png old mode 100644 new mode 100755 diff --git a/img/obstacle_detection/collision_detection.png b/img/obstacle_detection/collision_detection.png old mode 100644 new mode 100755 diff --git a/img/obstacle_detection/obstacle_detection.png b/img/obstacle_detection/obstacle_detection.png old mode 100644 new mode 100755 diff --git a/img/old_label_tool.png b/img/old_label_tool.png old mode 100644 new mode 100755 diff --git a/img/patches.png b/img/patches.png old mode 100644 new mode 100755 diff --git a/img/platform_interface.png b/img/platform_interface.png old mode 100644 new mode 100755 diff --git a/img/potential-field.png b/img/potential-field.png old mode 100644 new mode 100755 diff --git a/img/prob_compass.png b/img/prob_compass.png old mode 100644 new mode 100755 diff --git a/img/rc-dialogs/BehaviorViewer.png b/img/rc-dialogs/BehaviorViewer.png old mode 100644 new mode 100755 diff --git a/img/rc-dialogs/DebugRequests.png b/img/rc-dialogs/DebugRequests.png old mode 100644 new mode 100755 diff --git a/img/rc-dialogs/ExternalTools.png b/img/rc-dialogs/ExternalTools.png old mode 100644 new mode 100755 diff --git a/img/rc-dialogs/FieldViewer.png b/img/rc-dialogs/FieldViewer.png old mode 100644 new mode 100755 diff --git a/img/rc-dialogs/ImageViewer.png b/img/rc-dialogs/ImageViewer.png old mode 100644 new mode 100755 diff --git a/img/rc-dialogs/LogfileRecorder.png b/img/rc-dialogs/LogfileRecorder.png old mode 100644 new mode 100755 diff --git a/img/rc-dialogs/Modify.png b/img/rc-dialogs/Modify.png old mode 100644 new mode 100755 diff --git a/img/rc-dialogs/ModuleConfigurationViewer.png b/img/rc-dialogs/ModuleConfigurationViewer.png old mode 100644 new mode 100755 diff --git a/img/rc-dialogs/ParameterPanel.png b/img/rc-dialogs/ParameterPanel.png old mode 100644 new mode 100755 diff --git a/img/rc-dialogs/Plot2D.png b/img/rc-dialogs/Plot2D.png old mode 100644 new mode 100755 diff --git a/img/rc-dialogs/RepresentationInspector.png b/img/rc-dialogs/RepresentationInspector.png old mode 100644 new mode 100755 diff --git a/img/rc-dialogs/Simspark.png b/img/rc-dialogs/Simspark.png old mode 100644 new mode 100755 diff --git a/img/rc-dialogs/StopwatchViewer.png b/img/rc-dialogs/StopwatchViewer.png old mode 100644 new mode 100755 diff --git a/img/rc-dialogs/StopwatchViewer2018.png b/img/rc-dialogs/StopwatchViewer2018.png old mode 100644 new mode 100755 diff --git a/img/rc-dialogs/TeamCommLogViewer.png b/img/rc-dialogs/TeamCommLogViewer.png old mode 100644 new mode 100755 diff --git a/img/rc-dialogs/TeamCommLogViewer2018.png b/img/rc-dialogs/TeamCommLogViewer2018.png old mode 100644 new mode 100755 diff --git a/img/rc-dialogs/TeamCommViewer.png b/img/rc-dialogs/TeamCommViewer.png old mode 100644 new mode 100755 diff --git a/img/rc-dialogs/TeamCommViewer2018.png b/img/rc-dialogs/TeamCommViewer2018.png old mode 100644 new mode 100755 diff --git a/img/rc19-naoth-1024x683.jpg b/img/rc19-naoth-1024x683.jpg old mode 100644 new mode 100755 diff --git a/img/robotcontrol2018.png b/img/robotcontrol2018.png old mode 100644 new mode 100755 diff --git a/img/screenshot_open_teamcommlog.png b/img/screenshot_open_teamcommlog.png old mode 100644 new mode 100755 diff --git a/img/screenshot_play_teamcomm.png b/img/screenshot_play_teamcomm.png old mode 100644 new mode 100755 diff --git a/img/screenshot_teamcomm_fieldviewer.png b/img/screenshot_teamcomm_fieldviewer.png old mode 100644 new mode 100755 diff --git a/img/simulation.png b/img/simulation.png old mode 100644 new mode 100755 diff --git a/img/strategic_positions.png b/img/strategic_positions.png old mode 100644 new mode 100755 diff --git a/img/synchronization_tool.png b/img/synchronization_tool.png old mode 100644 new mode 100755 diff --git a/img/vision-modules.png b/img/vision-modules.png old mode 100644 new mode 100755 diff --git a/img/walking_engine.png b/img/walking_engine.png old mode 100644 new mode 100755 diff --git a/img/xabsl_editor.png b/img/xabsl_editor.png old mode 100644 new mode 100755 diff --git a/index.html b/index.html old mode 100644 new mode 100755 index 8503c00..071a88b --- a/index.html +++ b/index.html @@ -706,9 +706,35 @@ + +
        • + + + + + + + + + + +
          • + + + +
        • - - Handling the Robot + + Working with logfiles + +
          • + + + + + + + + + +
        • + + Sound
          • @@ -973,7 +1020,7 @@ -

          Welcome to Berlin United

          +

          Welcome to Berlin United

          Berlin United Team Photo

          Berlin United is a robot soccer team in the RoboCup Standard Platform League. We release most of our code on GitHub. @@ -1057,7 +1104,7 @@

          - + diff --git a/javascripts/config.js b/javascripts/config.js old mode 100644 new mode 100755 index 09fbd88..77d2e60 --- a/javascripts/config.js +++ b/javascripts/config.js @@ -1,16 +1,16 @@ -window.MathJax = { - tex: { - inlineMath: [["\\(", "\\)"]], - displayMath: [["\\[", "\\]"]], - processEscapes: true, - processEnvironments: true - }, - options: { - ignoreHtmlClass: ".*|", - processHtmlClass: "arithmatex" - } -}; - -document$.subscribe(() => { - MathJax.typesetPromise() +window.MathJax = { + tex: { + inlineMath: [["\\(", "\\)"]], + displayMath: [["\\[", "\\]"]], + processEscapes: true, + processEnvironments: true + }, + options: { + ignoreHtmlClass: ".*|", + processHtmlClass: "arithmatex" + } +}; + +document$.subscribe(() => { + MathJax.typesetPromise() }) \ No newline at end of file diff --git a/naoth_tools/gitlab-ci/index.html b/naoth_tools/gitlab-ci/index.html old mode 100644 new mode 100755 index 525d239..87bbcf2 --- a/naoth_tools/gitlab-ci/index.html +++ b/naoth_tools/gitlab-ci/index.html @@ -787,9 +787,35 @@ + +
        • + + + + + + + + + + +
          • + + + +
        • - - Handling the Robot + + Working with logfiles + +
          • + + + + + + + + + +
        • + + Sound
          • @@ -1117,9 +1164,9 @@ -

          Gitlab CI

          +

          Gitlab CI

          For our main repositories we utilize gitlab runner to build the code. You can specify jobs in a .gitlab-ci.yml file and set the execution order. Each job will run inside a docker container you specify. You can also built docker images within a job and later use those in another job. See ... The actual execution of the jobs is triggered by a gitlab-runner binary installed on Universities servers and two of our own lab servers (goal and ball server).

          -

          Usage

          +

          Usage

          The simplest example is to create a .gitlab-ci.yml in the repos root and upload it. 

          hello:
   script:
@@ -1135,7 +1182,7 @@ 
          Usage
          The tags for a runner are configured during the installation of the gitlab runner. Please note that if a runner has multiple tags you have to set all of them to select this runner. You can use the runner provided by the university and for jobs without special needs we recommend using them. But you can get rate limited there. If you have repos where you commit often you should set up your pipeline to not run on every commit.

          There are many more options to configure your CI pipeline. For reference please have a look at the existing ones or the official documentation at https://docs.gitlab.com/ee/ci/yaml/

          -

          Linux Toolchain

          +

          Linux Toolchain

          Code can be found at https://github.com/BerlinUnited/linuxtoolchain. This repo is a mirror from an internal repo.

          The following files are used for setting up the automatic compilation and deployment of the NaoTH Linux-Toolchain:
          - Dockerfile
          @@ -1156,7 +1203,7 @@

          Linux Toolchain

          Note that Docker only works with linux container when the base system is linux. More information can be found at: https://stackoverflow.com/questions/42158596/can-windows-containers-be-hosted-on-linux

          The execution of the linux toolchain pipeline will trigger the execution of the NaoTH pipeline.

          -

          NaoTH Pipeline

          +

          NaoTH Pipeline

          Code can be found at https://github.com/BerlinUnited/NaoTH. This repo is currently updated once a year.

          Note

          @@ -1173,12 +1220,12 @@

          NaoTH Pipeline

          The publish_naoth_docker job creates a docker image based on the linux toolchain with all the compile output. The docker image is published to the internal gitlab docker registry of the repository.

          The publish_naoth_python job publishes the naoth python package to the internal pip registry of the repository.

          -

          NaoImage Pipeline

          +

          NaoImage Pipeline

          This pipeline will run inside the image created by the naoth pipeline. It will build the softbank and the ubuntu image. This repo is still experimental. It is based on work done by the NaoDevils (currently unreleased).

          -

          Setup

          +

          Setup

          As mentioned above we set up gitlab-runner on our ball and goal server.

          -

          How to setup Gitlab Runner

          +

          How to setup Gitlab Runner

          We have a gitlab runner set up at ball.informatik.hu-berlin.de and goal.informatik.hu-berlin.de. Both servers are located in our lab.

          Install gitlab runner 

          # setup package repo
@@ -1193,7 +1240,7 @@ 
          How to setup Gitlab Runner
          
 
          runner config is located in /etc/gitlab-runner/config.toml
          
 
          enable gpus by adding stuff to the config.toml
 https://docs.gitlab.com/runner/configuration/gpus.html
          
-
          Cleanup Jobs
          
+
          Cleanup Jobs
          
 
          The Gitlab CI docker executor leaves a lot of volumes and images behind to speed up the CI jobs. To free up the disk a bit
 we run a cron job once every day to cleanup the CI cache volumes and old images. For this we added the following lines to
 the root crontab file (run sudo crontab -e to edit)
@@ -1278,7 +1325,7 @@ 
          Cleanup Jobs
          
     
          
       
          
     
          
-    
+    
     
     
       
diff --git a/naoth_tools/k8s/index.html b/naoth_tools/k8s/index.html
old mode 100644
new mode 100755
index 16fede1..4a642c6
--- a/naoth_tools/k8s/index.html
+++ b/naoth_tools/k8s/index.html
@@ -694,9 +694,35 @@
   
   
   
+    
+    
        • 
+      
+      
+        
+      
+      
+      
+      
+        
+      
+      
+    
          • 
+  
+
+            
+          
             
               
   
   
   
     
        • 
-      
-        Handling the Robot
+      
+        Working with logfiles
+      
+    
          • 
+  
+
+            
+          
+            
+              
+  
+  
+  
+    
        • 
+      
+        Sound
       
     
          • 
   
@@ -983,9 +1030,9 @@
                 
 
 
-
          NaoTH Kubenertes Cluster
          
-
          Setup
          
-
          Deployed Applications
          
+
          NaoTH Kubenertes Cluster
          
+
          Setup
          
+
          Deployed Applications
          
 
          TODO
          
 
               
@@ -1065,7 +1112,7 @@ 
          Deployed Applications
          
     
          
       
          
     
          
-    
+    
     
     
       
diff --git a/naoth_tools/lab-trackingsystem/index.html b/naoth_tools/lab-trackingsystem/index.html
old mode 100644
new mode 100755
index 950890d..36961b4
--- a/naoth_tools/lab-trackingsystem/index.html
+++ b/naoth_tools/lab-trackingsystem/index.html
@@ -543,62 +543,10 @@
         
       
       
-        
-      
       
         NaoTH Tracking System
       
       
-        
-
-
-      
     
   
 
@@ -760,9 +708,35 @@
   
   
   
+    
+    
        • 
+      
+      
+        
+      
+      
+      
+      
+        
+      
+      
+    
          • 
+  
+
+            
+          
             
               
   
   
   
     
        • 
-      
-        Handling the Robot
+      
+        Working with logfiles
+      
+    
          • 
+  
+
+            
+          
+            
+              
+  
+  
+  
+    
        • 
+      
+        Sound
       
     
          • 
   
@@ -1015,42 +1010,6 @@
     
   
   
-    
-    
-
          @@ -1063,16 +1022,16 @@ -

          NaoTH Tracking System

          -

          Preparations

          +

          NaoTH Tracking System

          +

          Preparations

          You have to run the following code on the robot to enable multicast
          sudo route add -net 224.0.0.0 netmask 240.0.0.0 dev wlan0

          Multicast must be enabled before the tracking system code works on the robot. Otherwise you will get a segfault.

          -

          Configure the recording

          +

          Configure the recording

          TODO make images showing the workflow

          -

          Calibrate the system

          +

          Calibrate the system

          TODO

          -

          Recording

          +

          Recording

          TODO: mention tips for optimal results

          @@ -1152,7 +1111,7 @@

          Recording

          - + diff --git a/naoth_tools/naoscp/index.html b/naoth_tools/naoscp/index.html old mode 100644 new mode 100755 index f23ece1..1cd1110 --- a/naoth_tools/naoscp/index.html +++ b/naoth_tools/naoscp/index.html @@ -746,9 +746,35 @@ + +
        • + + + + + + + + + + +
          • + + + +
        • - - Handling the Robot + + Working with logfiles + +
          • + + + + + + + + + +
        • + + Sound
          • @@ -1035,7 +1082,7 @@ -

          NaoSCP

          +

          NaoSCP

          NaoSCP is a tool created by us which helps to deploy our code to the NAO. We provide it as a netbeans project inside our NaoTH repository. The netbeans project is located at <NaoTH-Projekt/Naoth-2020>/NaoSCP. Inside netbeans you can open it with: File->Import Project <ProjectDir>/NaoSCP

          @@ -1053,7 +1100,7 @@
        • TODO add nao scp docu in extra site here and remove it from teamreport
        • TODO: add note about alias in bashrc for gradle run/build naoscp
        -

        Initialize Robot

        +

        Initialize Robot

        This will prompt you for the extern/lib directory. This could be for example /home/myuser/robocup/repositories/linuxtoolchain/toolchain_nao/extern/lib

        After that you are prompted for the current IP address of the robot.

        @@ -1946,7 +1993,7 @@

        Initialize Robot

        The system is going down for reboot NOW!
    -

    Send to robot

    +

    Send to robot

    You need to set the head number and select what you want to deploy.

    Send to robot output @@ -2818,7 +2865,7 @@

    Send to robot

    - + diff --git a/naoth_tools/nvidia/index.html b/naoth_tools/nvidia/index.html old mode 100644 new mode 100755 index bf92d65..411d683 --- a/naoth_tools/nvidia/index.html +++ b/naoth_tools/nvidia/index.html @@ -694,9 +694,35 @@ + +
  • + + + + + + + + + + +
    • + + + +
  • - - Handling the Robot + + Working with logfiles + +
    • + + + + + + + + + +
  • + + Sound
    • @@ -961,7 +1008,7 @@ -

    Setup Nvidia Driver on Lab Servers

    +

    Setup Nvidia Driver on Lab Servers

    Goal Server: sudo ubuntu-drivers install

    then you have nvidia-smi available

    @@ -1045,7 +1092,7 @@
    - + diff --git a/naoth_tools/rc/index.html b/naoth_tools/rc/index.html old mode 100644 new mode 100755 index 001eb56..0776d37 --- a/naoth_tools/rc/index.html +++ b/naoth_tools/rc/index.html @@ -739,9 +739,35 @@ + +
  • + + + + + + + + + + +
    • + + + +
  • - - Handling the Robot + + Working with logfiles + +
    • + + + + + + + + + +
  • + + Sound
    • @@ -1021,7 +1068,7 @@ -

    RoboControl

    +

    RoboControl

    Some explanations of commons tasks we do with RobotControl.

    Note

    @@ -1033,7 +1080,7 @@
    • TODO: add note about alias in bashrc for gradle run/build naoscp
    -

    View Teamcommlogs in Robotcontrol

    +

    View Teamcommlogs in Robotcontrol

    In RobotControl open the TeamCommLogViewer Dialog and open a teamcomm logfile. Open a teamcomm log file

    Open the FieldViewer and the TeamCommViewer FX Dialogs next to each other. In the FieldViewer click on the log button and then @@ -1121,7 +1168,7 @@

    View Teamcommlogs in Robotcontrol

    - + diff --git a/naoth_tools/simspark/index.html b/naoth_tools/simspark/index.html old mode 100644 new mode 100755 index 10035de..19fd8ca --- a/naoth_tools/simspark/index.html +++ b/naoth_tools/simspark/index.html @@ -759,9 +759,35 @@ + +
  • + + + + + + + + + + +
    • + + + +
  • - - Handling the Robot + + Working with logfiles + +
    • + + + + + + + + + +
  • + + Sound
    • @@ -1061,19 +1108,19 @@ -

    Simspark

    -

    Create a Simspark-Appimage

    +

    Simspark

    +

    Create a Simspark-Appimage

    AppImage is a container holding all the neccessary binaries and libraries for starting a specifiic application: https://appimage.org/

    Useful example AppImage-Howto: https://www.booleanworld.com/creating-linux-apps-run-anywhere-appimage/

    To create an AppImage for Simspark i've basically used the following description for creating an AppImage:
    https://github.com/AppImage/AppImageKit/wiki/Creating-AppImages#6-manually-create-an-appdir

    -

    Variant # 1

    +

    Variant # 1

    • installed all necessary libraries and requrirements for simspark
    • used the script for compiling an appimage: simspark.sh
    -

    Variant # 2

    +

    Variant # 2

    To get a "clean" environment for "installing" (compiling) the Simspark binary i used a chroot environment and pulled all necessary files from there. For the chroot environment i used this script:
    https://github.com/boolean-world/appimage-resources/blob/master/tempenv.sh

    @@ -1169,7 +1216,7 @@

    Variant # 2

    - + diff --git a/naoth_tools/webots/index.html b/naoth_tools/webots/index.html old mode 100644 new mode 100755 index 4500555..9a0cdd2 --- a/naoth_tools/webots/index.html +++ b/naoth_tools/webots/index.html @@ -708,9 +708,35 @@ + +
  • + + + + + + + + + + +
    • + + + +
  • - - Handling the Robot + + Working with logfiles + +
    • + + + + + + + + + +
  • + + Sound
    • @@ -975,7 +1022,7 @@ -

    Webots

    +

    Webots

    @@ -1054,7 +1101,7 @@

    Webots

    - + diff --git a/naoth_tools/xabsleditor/index.html b/naoth_tools/xabsleditor/index.html old mode 100644 new mode 100755 index bc57960..424b230 --- a/naoth_tools/xabsleditor/index.html +++ b/naoth_tools/xabsleditor/index.html @@ -708,9 +708,35 @@ + +
  • + + + + + + + + + + +
    • + + + +
  • - - Handling the Robot + + Working with logfiles + +
    • + + + + + + + + + +
  • + + Sound
    • @@ -975,7 +1022,7 @@ -

    Xabsleditor

    +

    Xabsleditor

    • TODO add xabsleditor docu in extra site here and remove it from teamreport
    @@ -1057,7 +1104,7 @@
    - + diff --git a/robocup_tools/game_recording/index.html b/robocup_tools/game_recording/index.html old mode 100644 new mode 100755 index 375018f..aea454d --- a/robocup_tools/game_recording/index.html +++ b/robocup_tools/game_recording/index.html @@ -801,9 +801,35 @@ + +
  • + + + + + + + + + + +
    • + + + +
  • - - Handling the Robot + + Working with logfiles + +
    • + + + + + + + + + +
  • + + Sound
    • @@ -1145,14 +1192,14 @@ -

    Game Recording

    +

    Game Recording

    We want to be able to record RoboCup games in synchronized fashion. To achieve this we created a setup which records the game bases on GameController signals.

    The raspberry receives game controller messages over it's LAN interface. The Wifi interface is connected the GoPros own wireless network. When the Pi receives a READY, SET or PLAY signal from the gamecontrller it sends in turn a signal to the gopro to start a recording. The recording is stopped when the Pi receives a FINISH or INITIAL signal or if it does not receive a message from the gamecontroller for 5 seconds.

    -

    Hardware Setup

    +

    Hardware Setup

    The Setup consists of a tripod base, two tripod extensions, a mounting plate with a tripod ball head, a raspberry pi, gopro + cables. The cable setup is slightly different for gopro sessions vs gopro hero 5. For more details on which parts we bought and what alterations we did see the advanced setup section at the bottom.

    @@ -1170,7 +1217,7 @@

    Hardware Setup

    tape over the data lines of the usb cable on the usb 2 end. The data lines are the two in the middle. Also the camera will only charge if it is not recording.

    USB cable data lines

    -

    Software Setup

    +

    Software Setup

    Our Pi's have configured IP addresses like 10.0.4.X Where X is the number written on the Pi 

      IP Address: 10.0.4.x
   mask: 255.255.0.0
@@ -1184,7 +1231,7 @@ 
    Software Setup
    
 
      sudo nano /etc/dhcpcd.conf
 
    
 
    On the GoPro you need to start Wifi manually in the camera settings.
    
-
    LED's
    
+
    LED's
    
 
    Die blaue LED zeigt den Status der Verbindung zur GoPro an.
    
 - schnelles Blinken: bedeutet das der Raspberry Pi sich nicht zu dem Wlan der GoPro verbinden kann.
    
 - langsames Blinken: bedeutet das der Raspberry Pi das Wlan der GoPro sieht aber noch nicht verbunden ist.
    
@@ -1196,18 +1243,18 @@ 
    LED's
    
 
    Die rote LED zeigt den Status der Aufnahme an.
    
 - blinken: zeigt an das ein Signal für die Aufnahme an die GoPro gesendet wird. An der Vorder- und Hinterseite der 
 GoPro sollten auch jeweils eine rote LED blinken, was bedeutet, dass die GoPro tatsächlich aufnimmt.  
    
-
    Vor dem Spiel - Bekanntes Problem
    
+
    Vor dem Spiel - Bekanntes Problem
    
 
    In der aktuellen Realisierung schaltet sich der WLAN der GoPro regelmäßig ab. Es sollte rechtzeitig vor dem Spiel geprüft werden ob die Verbindung besteht (blaue LED leuchtet konstant). Falls nicht, dann muss das WLAN der GoPro neu gestartet werden.   
    
 
    Dazu wird wie folgt verfahren: Zum Anschalten der des Wlans muss an der Seite die Mode Taste gedrückt werden. Anschließend auf dem Display von oben nach unten wischen um ins Menü zu kommen. In dem Menü muss auf Connect gedrückt werden und im Submenü ganz unten kann das Wlan an- und wieder ausgeschaltet werden.  
    
-
    Nach dem Spiel/Spieltag
    
+
    Nach dem Spiel/Spieltag
    
 
    Der GameController nimmt für jedes Spiel ein Log und ein TeamComm Log auf. Diese müssen bisher von Hand gesichert werden. Für uns es ist wichtig das wir diese Daten bekommen. Die Logs liegen im logs  und logs_teamcomm Ordner neben der GameController.jar Datei.  
    
 
    Die Daten der GoPro SD Karte müssen nach einem Spieltag auf ein externes Speichermedium gesichert werden um Platz zu schaffen für die Aufnahmen des nächsten Tages. 
    
-
    Video tutorial for setup in german
    
+
    Video tutorial for setup in german
    
 
    
-
    Advanced Setup:
    
-
    Alterations to the hardware
    
+
    Advanced Setup:
    
+
    Alterations to the hardware
    
 
    TODO
    
-
    Hardware List:
    
+
    Hardware List:
    
 
    Tripod Head: Amazon
 TODO
    @@ -1288,7 +1335,7 @@

    Hardware List:

    - + diff --git a/robocup_tools/index.html b/robocup_tools/index.html old mode 100644 new mode 100755 index e729b64..166b004 --- a/robocup_tools/index.html +++ b/robocup_tools/index.html @@ -708,9 +708,35 @@ + +
  • + + + + + + + + + + +
    • + + + +
  • - - Handling the Robot + + Working with logfiles + +
    • + + + + + + + + + +
  • + + Sound
    • @@ -975,7 +1022,7 @@ -

    RoboCup Project

    +

    RoboCup Project

    TODO describe our recording Project here in detail

    @@ -1055,7 +1102,7 @@
    - + diff --git a/sitemap.xml b/sitemap.xml old mode 100644 new mode 100755 index 158a1a7..dd730dc --- a/sitemap.xml +++ b/sitemap.xml @@ -2,187 +2,197 @@ None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 daily None - 2024-04-02 + 2024-04-24 + daily + + + None + 2024-04-24 + daily + + + None + 2024-04-24 daily \ No newline at end of file diff --git a/sitemap.xml.gz b/sitemap.xml.gz old mode 100644 new mode 100755 index 556d9c021655a4bc9f3eb461029ad802b828b10c..25e2c7552c39b6c4d1139c7848ce311f7408325c GIT binary patch literal 220 zcmV<203-h&iwFohKq+Pd|8r?{Wo=<_E_iKh0PW4a4uUWg1>oJMAl;!z7}S)qIXdYB z5U9|kw8*WZZ!do_Jb}}>OK(q4`icDAn&72+(#~dVAH^(Cwse)Yb;fSjb9i96+=*vz zNcmtaTgq7DeRtw~7>01T z1^L!hGKrHkjMFel$I6;*YGqT{THSl~kX510NAOLui2O$a2M!!KaNxj!0|yQqIB?*= WfddErAACMYdHDsz{IoH66951Z!fg!z literal 219 zcmV<103`n(iwFpKYz$@s|8r?{Wo=<_E_iKh0PW394uUWc1>ilWXu5|YaiOM^ol94G z00I?Aphc#N-rn*P!wFoyNs~^eFZ0Rl-L>qc2WqTI`7Vk%)26UxW2%(juIKQ;GZo0^ z(2{n{hYh8?cCJ4OF^*$IjniG;Q)GuqP*M8_iFGLA=*U&Td`%0MZZ6r{LS3xUQcT_U zBPZ9{QYCS+4`Y84A6aLcSW)Mu()U3>RGByJM~cm45yg)L4jede;J|?c2M!!KaNxj! V0|)+Je7;CK^#$}h*!X@D008h%W?%pS diff --git a/stylesheets/extra.css b/stylesheets/extra.css old mode 100644 new mode 100755 index e99f7e6..1ed10fd --- a/stylesheets/extra.css +++ b/stylesheets/extra.css @@ -1,7 +1,7 @@ -.md-sidebar--secondary{ - visibility: hidden !important; - width: 0; -} -.md-grid { - max-width: 75rem; +.md-sidebar--secondary{ + visibility: hidden !important; + width: 0; +} +.md-grid { + max-width: 75rem; } \ No newline at end of file diff --git a/teamreport/architecture_intro/index.html b/teamreport/architecture_intro/index.html old mode 100644 new mode 100755 index 6113c9c..c3f957b --- a/teamreport/architecture_intro/index.html +++ b/teamreport/architecture_intro/index.html @@ -773,9 +773,35 @@ + +
  • + + + + + + + + + + +
    • + + + +
  • - - Handling the Robot + + Working with logfiles + +
    • + + + + + + + + + +
  • + + Sound
    • @@ -1089,7 +1136,7 @@ -

    Architecture

    +

    Architecture

    An appropriate architecture is the base of each successful software project. It enables a group of developers to work at the same project and to organize the solutions for their particular research questions. @@ -1105,7 +1152,7 @@ implementation of different parts of the architecture. A detailed description of the principles we used can also be found in [@Mellmann2010b].

    -

    NaoSMAL

    +

    NaoSMAL

    In our architecture we don't use the NAOqi API directly but use our own so-called NaoSMAL (Nao Shared Memory Abstraction Layer) NAOqi-module. This calls the DCM API of NAOqi @@ -1116,7 +1163,7 @@

    NaoSMAL

    will continue to run and ensures the robot will go in a stable position), faster redeploy of our binary without restarting NAOqi and a faster compilation since we have fewer dependencies.

    -

    Platform Interface

    +

    Platform Interface

    In order to integrate different platforms, our project is divided into two parts: a platform independent one and platform specific one. The platform specific part contains code which is applied to the particular @@ -1129,7 +1176,7 @@

    Platform Interface

    Platform Interface is responsible for data transfer and execution of the Cognition and Motion processes.

    -

    Module framework

    +

    Module framework

    Our module framework is based on a blackboard architecture. The framework consists of the following basic components:

    Representation: objects carrying data and simple manipulation functions

    @@ -1147,7 +1194,7 @@

    Module framework

    functionality and provide the same representations. You can configure which of the modules should be executed at runtime and it is also possible to dynamically change this for debugging purposes.

    -

    Example module

    +

    Example module

    A module is a C++ class which inherits a base class which is created with the help of some macros defining the interface of the the module.

    #ifndef _MyModule_H
@@ -1225,7 +1272,7 @@ 
    Example module
    
 
    REGISTER_MODULE(MyModule);
 
    
 
    The order of registration defines the order of execution of the modules.
    
-
    Serialization
    
+
    Serialization
    
 
    As described in the 
 Section 2.3 the core of the program is structured in
 modules which are responsible for different tasks like image processing,
@@ -1405,7 +1452,7 @@ 
    Serialization
    
     
    
       
    
     
    
-    
+    
     
     
       
diff --git a/teamreport/behavior/index.html b/teamreport/behavior/index.html
old mode 100644
new mode 100755
index 0a06045..6c904c8
--- a/teamreport/behavior/index.html
+++ b/teamreport/behavior/index.html
@@ -753,9 +753,35 @@
   
   
   
+    
+    
  • 
+      
+      
+        
+      
+      
+      
+      
+        
+      
+      
+    
    • 
+  
+
+            
+          
             
               
   
   
   
     
  • 
-      
-        Handling the Robot
+      
+        Working with logfiles
+      
+    
    • 
+  
+
+            
+          
+            
+              
+  
+  
+  
+    
  • 
+      
+        Sound
       
     
    • 
   
@@ -1049,7 +1096,7 @@
                 
 
 
-
    Behavior
    
+
    Behavior
    
 
    The Extensible Agent Behavior Specification Language | XABSL
 cf. [@xabsl_web] is a behavior description language for autonomous
 agents based on hierarchical finite state machines. XABSL is originally
@@ -1082,7 +1129,7 @@
 Figure 7.2{reference-type="ref"
 reference="fig:xabsleditor"} (right) illustrates the execution tree of
 the behavior shown within the visualizer.
    
-
    Team Strategy
    
+
    Team Strategy
    
 
    Our team strategy is based on the notion of the active and passive
 behavior. Active behavior defines for each robot what is to do if the
 robot is at the ball, while passive behavior describes what is to do if
@@ -1132,7 +1179,7 @@ 
    Team Strategy
    
 assignment of ready positions to the players. Dashed arrows illustrate
 the transitions from passive kickoff positions to the corresponding
 passive positions.
    
-
    Role Change
    
+
    Role Change
    
 
    The task of the role decision mechanism is to determine who is striker
 and who's passive. In general there should only be one robot which acts
 as striker. This year we loosened the "only one striker" condition in
@@ -1245,7 +1292,7 @@ 
    Role Change
    
 often kick the "real" ball, even if the first striker sees a wrong ball.
    
 
    As last safeguard, if there's only one player left, this player becomes
 striker.
    
-
    Teamball
    
+
    Teamball
    
 
    The teamball represents a hypothesis of the real ball, based on the ball observations of each player and consolidated to a single position on the field.
    
 
    The teamball is then used to adjust the search behavior of passive robots and the goalie. In the past years, when the robot was passive and searched for the ball, the robot turned in one direction (randomly) and moves his head to find the ball. With the teamball the robot changes its behavior. Before the robot starts a new search rotation he turns to the teamball and looks in that direction in order to find the ball. If he doesn't see the ball, he continues to turn in the same turning direction as before, until a full round is completed. Before starting a new round, the robot takes again a look at the teamball and re-starts the described behavior. The ball search continues until the robot found the ball or another game event occurs.
    
 
    In 2017 we reintroduced the teamball in order to improve our ball search behavior. Therefor each player communicated its ball model and collected the global ball position of its teammates over a certain amount of time. In each cognition cycle the median in x and y position of all collected balls where determined and used as teamball in the above described behavior. If there wasn't an update for some time, the teamball was invalidated and not used anymore.
    
@@ -1329,7 +1376,7 @@ 
    Teamball
    
     
    
       
    
     
    
-    
+    
     
     
       
diff --git a/teamreport/bibliography/index.html b/teamreport/bibliography/index.html
old mode 100644
new mode 100755
index 84cc3d3..e0b3727
--- a/teamreport/bibliography/index.html
+++ b/teamreport/bibliography/index.html
@@ -701,9 +701,35 @@
   
   
   
+    
+    
  • 
+      
+      
+        
+      
+      
+      
+      
+        
+      
+      
+    
    • 
+  
+
+            
+          
             
               
   
   
   
     
  • 
-      
-        Handling the Robot
+      
+        Working with logfiles
+      
+    
    • 
+  
+
+            
+          
+            
+              
+  
+  
+  
+    
  • 
+      
+        Sound
       
     
    • 
   
@@ -1047,7 +1094,7 @@ 
    Bibliography
    
     
    
       
    
     
    
-    
+    
     
     
       
diff --git a/teamreport/debugging/index.html b/teamreport/debugging/index.html
old mode 100644
new mode 100755
index b48553f..c87c804
--- a/teamreport/debugging/index.html
+++ b/teamreport/debugging/index.html
@@ -510,19 +510,6 @@
     NaoSCP
   
   
-    
-
  • @@ -530,46 +517,6 @@ USB flash drive - -
    • @@ -590,19 +537,6 @@ Logfile Format - - @@ -984,9 +918,35 @@ + +
  • + + + + + + + + + + +
    • + + + +
  • - - Handling the Robot + + Working with logfiles + +
    • + + + + + + + + + +
  • + + Sound
    • @@ -1390,19 +1371,6 @@ NaoSCP - -
  • @@ -1410,46 +1378,6 @@ USB flash drive - -
    • @@ -1470,19 +1398,6 @@ Logfile Format - - @@ -1511,7 +1426,7 @@ -

    Debugging and Tools

    +

    Debugging and Tools

    In order to develop a complex software for a mobile robot, we require means for high-level debugging and monitoring (e. g., visualization of the robot's posture or its position on the field). Since we do not @@ -1522,7 +1437,7 @@ visualization software like RobotControl, as shown in Figure 3.1{reference-type="ref" reference="fig:robotcontrol"}.

    -

    Concepts

    +

    Concepts

    Some of the ideas were evolved from the GT-Architecture [@2007:RoboCup-TDP-GermanTeam]. The following list illustrates some of the debug concepts:

    @@ -1562,7 +1477,7 @@

    Concepts

    reference="fig:robotcontrol"} illustrates visualizations of the debug concepts. In particular a field view, 3D view, behavior tree, plot and the table of debug requests are shown.

    -

    RobotControl

    +

    RobotControl

    The various debugging possibilities are organized in different dialogs. In this year we started to update the UI by switching to JavaFX for some dialogs. The following list consists of our most used RobotControl @@ -1579,7 +1494,7 @@

    RobotControl

    -

    Behavior Viewer

    +

    Behavior Viewer

    @@ -1590,7 +1505,7 @@

    Behavior Viewer

    -

    Debug Requests

    +

    Debug Requests

    @@ -1601,42 +1516,42 @@

    Debug Requests

    -

    Field Viewer

    +

    Field Viewer

    image

    There are views for different field sizes and a local view. Certain debug requests draw on these views. For example, you could draw the robots' positions on the field by activating the corresponding debug request.

    -

    Image Viewer

    +

    Image Viewer

    image

    Can show the top and bottom images. There are debug requests that draw on the camera images, if they are active.

    -

    Logfile Recorder

    +

    Logfile Recorder

    image

    Records a log file on a robot with manually selected representations.

    -

    Modify

    +

    Modify

    image

    The Modify macro allows changing values of variables declared within this macro at runtime.

    -

    Module Configuration Viewer

    +

    Module Configuration Viewer

    image

    Shows which modules are currently (de-)activated. Also indicates, which other modules are required (left) and provided (right) by each module.

    -

    Parameter Panel

    +

    Parameter Panel

    image

    Shows parameters defined in our configuration files. It is possible to change the values at runtime. The variables must be registered as parameters in the code.

    -

    Plot 2D

    +

    Plot 2D

    image

    Shows plots activated by plot debug requests.

    -

    Representation Inspector

    +

    Representation Inspector

    image

    Shows the data that is written to the blackboard by each representation.

    -

    Stopwatch Viewer

    +

    Stopwatch Viewer

    image

    Shows the execution time for each module.

    -

    Team Communication Viewer

    +

    Team Communication Viewer

    image

    Shows all connected robots and possible all of their provided status information via TeamComm (e.g. ip address, battery charge, temperature, @@ -1649,20 +1564,20 @@

    Team Communication Viewer

    reference="rc:fieldviewer"}. It's also useful for debugging the team communication to get a general overview of the role decision and its transitions. This year, the dialog was re-implemented using JavaFX.

    -

    Team Communication Logviewer

    +

    Team Communication Logviewer

    image

    The Team Communication Logviewer allows to re-play team communication logfiles previously recorded with the Team Communication Viewer. It's also able to re-play logs from the GameController and export them as JSON.

    -

    Simspark

    +

    Simspark

    image

    This dialog shows the state of the connected simspark instance. It also provides some predefined commands and a textfield for user-defined commands to send to simspark. Furthermore it also provides the teamcommunication of the simulated robots for other dialogs in RobotControl (e.g. Team Communication Viewer).

    -

    External Tools

    +

    External Tools

    image

    Some "external tools" where modified in order to load them into RobotControl via reflection. Its convenient to have all needed tools in @@ -1671,13 +1586,13 @@

    External Tools

    NaoSCP), for the setup & deployment of a robot, and a modified version of RoboViz, for visualization of a SimSpark simulation, integrated in RobotControl.

    -

    Robot Setup and Deployment

    +

    Robot Setup and Deployment

    In this section we give an overview over our deployment and robot setup procedure. Changing the configuration of the robot, e.g., deployment of the binary, network setup etc., is a critical point during both, development and competition. To minimize the chance of error we developed a set of procedures and tools.

    -

    Deployment Procedure

    +

    Deployment Procedure

    Currently we have two different deployment procedures:

    • @@ -1710,7 +1625,7 @@

      Deployment Procedure

      different sounds. This way the state of the robot and the progress of the deployment can be easily monitored, this is especially helpful when setting up a whole team before a game.

      -

      NaoSCP

      +

      NaoSCP

      NaoSCP is a setup and deployment tool. It primarily has three tasks: (1) initialize a new robot, e.g., copy libraries and scripts, (2) set the network configuration and (3) deploy naoth binary and configurations @@ -1728,7 +1643,7 @@

      NaoSCP

      bottom tool bar trigger particular deployment and setup actions like writing the network configuration to the robot or copying a new binary to a deployment USB flash drive.

      -

      Usage Remarks

      +

      Usage Remarks

      The following describes the particular components of the NaoSCP user interface as illustrated in the Figure 3.2.

      @@ -1762,7 +1677,7 @@

      Usage Remarks

      binaries and for automatic mounting and running of USB flash drives. Additionally the network is configured and the binary is deployed like previously described;

      -

      USB flash drive

      +

      USB flash drive

      A deployment flash drive can be created manually or (as described above) via the NaoSCP tool. As the minimum requirement there should be an executable shell script named \"'startBrainwashing.sh\"'. When the flash @@ -1773,13 +1688,13 @@

      USB flash drive

      deployment procedure are indicated by different sounds. This way is preferred when deploying software on several robots, e.g., setting up a team before a game.

      -

      Flash drive variants

      +

      Flash drive variants

      Currently we have different kinds of USB flash drives to accomplish different kinds of deployment or collection tasks.

      -
      Deploying
      +
      Deploying

      As described above the deployment flash drive is used to copy new binaries and/or configuration files to the robot.

      -
      Collecting log files
      +
      Collecting log files

      The \"'collect log files\"' flash drive is used to copy the recorded log file from the robot. This is primarily used after games to be able to analyze possible misbehavior of the robot. Otherwise, if the robot is @@ -1790,11 +1705,11 @@

      Collecting log files
      of the robot/binary of the log file and giving a quick overview on later log file examination. Finally we're collecting all \"'heard\"' whistles for later fine-tuning of our whistledetector.

      -
      Setting network
      +
      Setting network

      The network flash drive is used to set the network configuration of the robot (like IP address, etc.). Especially for the Wi-Fi configuration this method is useful to quickly set up all need robots before a game.

      -

      Logging

      +

      Logging

      Analysis and evaluation of the algorithms running on the robot is a big challenge.

      Our team has a long history in logging (our logs from 2010 are still @@ -1841,7 +1756,7 @@

      Logging

      iamge Synchronization Interface for individual log files and videos of a game.

      -

      Logfile Format

      +

      Logfile Format

      As described in the Section 2.3{reference-type="ref" reference="s:module_framework"} the state of the robot is stored in @@ -1867,7 +1782,7 @@

      Logfile Format

      2 CameraMatrix ... ...       -
      Annotate and Evaluate Logs
      +
      Annotate and Evaluate Logs

      Annotation interface was created and used to annotate different kick actions executed by our robots in the videos recorded during the games at the RoboCup in 2015. The kick events were automatically extracted @@ -1890,7 +1805,7 @@

      Annotate and Evaluate Logs
      estimation of its position, ball model and obstacles. On the left are three categories of binary labels describing the quality of the action.

      -

      Simulation

      +

      Simulation

      As a common experience, there are big gaps between simulation and reality in robotics, especially with regards to basic physics with consequences for low level skills in motion and perception. There are @@ -2020,7 +1935,7 @@

      Simulation

      - + diff --git a/teamreport/index.html b/teamreport/index.html old mode 100644 new mode 100755 index d56eb04..fb17240 --- a/teamreport/index.html +++ b/teamreport/index.html @@ -708,9 +708,35 @@ + +
    • + + + + + + + + + + +
      • + + + +
    • - - Handling the Robot + + Working with logfiles + +
      • + + + + + + + + + +
    • + + Sound
      • @@ -975,7 +1022,7 @@ -

      Welcome

      +

      Welcome

      Note

      This teamreport is only a high level overview of the team and software architecture used for the Nao's during RoboCup. @@ -1081,7 +1128,7 @@

      - + diff --git a/teamreport/introduction/index.html b/teamreport/introduction/index.html old mode 100644 new mode 100755 index ca1697a..4b58ce1 --- a/teamreport/introduction/index.html +++ b/teamreport/introduction/index.html @@ -746,9 +746,35 @@ + +
    • + + + + + + + + + + +
      • + + + +
    • - - Handling the Robot + + Working with logfiles + +
      • + + + + + + + + + +
    • + + Sound
      • @@ -1035,7 +1082,7 @@ -

      Introduction

      +

      Introduction

      This document gives an overview over the current state of the software base developed by the Berlin United - Nao Team Humboldt to control a humanoid robot in the context of the robot soccer competition RoboCup. @@ -1044,7 +1091,7 @@ links:

      Docu: https://github.com/BerlinUnited/NaoTH/wiki

      Code: https://github.com/BerlinUnited/NaoTH

      -

      Team

      +

      Team

      The research group NaoTH is part of the research lab for Adaptive Systems at Humboldt-Universität zu Berlin headed by Prof. Verena Hafner. At the current state the core team consists of about 12 students of @@ -1077,7 +1124,7 @@

      Team

      achieved 2nd place in both years. In 2017 NaoTH won the 2nd place in the challenger shield and in 2018 reached the quarterfinals in the champions cup of the main competition.

      -

      Summary

      +

      Summary

      Our general research fields include agent-oriented techniques and Machine Learning with applications in Cognitive Robotics. Currently, we mainly focus on the following topics:

      @@ -1179,7 +1226,7 @@

      Summary

      - + diff --git a/teamreport/modeling/index.html b/teamreport/modeling/index.html old mode 100644 new mode 100755 index 6d1f59b..5131f02 --- a/teamreport/modeling/index.html +++ b/teamreport/modeling/index.html @@ -448,33 +448,6 @@ The algorithm - -
    • @@ -849,9 +822,35 @@ + +
    • + + + + + + + + + + +
      • + + + +
    • - - Handling the Robot + + Working with logfiles + +
      • + + + + + + + + + +
    • + + Sound
      • @@ -1165,33 +1185,6 @@ The algorithm - -
    • @@ -1241,10 +1234,10 @@ -

      Modeling

      +

      Modeling

      In order to realize a complex and successful cooperative behavior it is necessary to have a appropriate model of the surrounding world. In our approach we focus on local models of particular aspect

      -

      Camera Matrix Calibration

      +

      Camera Matrix Calibration

      Camera matrix is the coordinate transformation of a camera in relation to the local coordinate system of the robot. The camera matrix is used to establish the relation between objects detected in the image and their position relative to the robot. For instance, the center of the detected @@ -1279,7 +1272,7 @@

      Camera Matrix Calibration

      -

      Probabilistic Compass

      +

      Probabilistic Compass

      We estimate the orientation of the robot on the field based on the detected line edgels utilizing the fact, that all field lines are either orthogonal or parallel to the field. Based on the orientations of the particular projected edgels it is possible to estimate the rotation of the robot up to the \(\pi\) symmetry.

      @@ -1311,7 +1304,7 @@

      Probabilistic Compass

      -

      Multi-Hypothesis-Extended-Kalman-Filter Ball Model

      +

      Multi-Hypothesis-Extended-Kalman-Filter Ball Model

      Although there is usually only one ball involved in a RoboCup game, there are several good reasons for being able to represent and track several ball hypotheses at the same time. The main reason however are the false-positives. Due to the change to the new black and white ball as described in the @@ -1333,7 +1326,7 @@

      Multi-Hypothesis-Ext Each measurement is assigned at most to one hypothesis and vice versa. If no matching hypothesis is found a new Kalman filter is created which will represent a new hypothesis. The final ball model for the behavior is the hypothesis which is the closest to the robot and is updated frequently.

      -

      Multi-Hypothesis Goal Model (MHGM)

      +

      Multi-Hypothesis Goal Model (MHGM)

      In this section we describe a multi-hypothesis approach for modeling a soccer goal within the RoboCup context.

      The whole goal is rarely observed and we assume the image processing to detect separate goal posts. So we represent the goal by its corresponding posts. To reduce complexity of the shape of uncertainty we model the separate goal posts in local robot coordinates. The ambiguous goal posts are tracked by a multi-hypothesis particle filter. The actual goal model is extracted from the set of post hypotheses.

      The joint uncertainty can be subdivided into noise, false detections and ambiguity. Each of these components is treated separately in our approach.

      @@ -1344,14 +1337,14 @@

      Multi-Hypothesis Goal Model (MHGM)

      More detailed description of the algorithm as well as the experimental results can be found in [@HSR-ScheunemannMellmann-14].

      image

      -

      Simulation Based Selection of Actions

      +

      Simulation Based Selection of Actions

      The robot is capable of different kicks and should given a particular situation, e.g., the robot's position, the position of the ball and obstacles, determine which kick is the optimal kick to perform in this situation. A naive geometric solution which selects a kick based on the robot's direction towards the opponent goal does not account for uncertainty of the actual execution of the kick. Furthermore the distance of the kick is not considered in this approach. An improved kick selection algorithm was developed which is based on a forward simulation of the actions. Thereby each possible kick is simulated and the best kick is chosen based on the outcome, i.e., the position of the ball after the kick. Uncertainty and additional constraints can be integrated in a straight forward way.

      -

      Definition of an Action

      +

      Definition of an Action

      An Action is a set of parameters which describe a probability distribution of the possible ball location after the execution of a kick. Currently there are 4 kicks, two forward kicks and two sidekicks @@ -1368,7 +1361,7 @@

      Definition of an Action

      -

      Determine the parameters

      +

      Determine the parameters

      To calculate the initial velocity of a kick the distance the ball rolled after a kick was measured in an experiment. By using the stopping distance formula the initial velocity of one kick can be calculated by

      @@ -1407,17 +1400,17 @@

      Determine the parameters

      a hypothetical kick. The end points are calculated by drawing a sample from both the angle and kick speed distribution and plugging these values in the last equation. For details, refer to the next section.

      -

      The algorithm

      +

      The algorithm

      The whole simulation is divided into three steps: simulate the consequences, evaluate the consequences and decide the best action.

      -

      Simulating the consequences

      +

      Simulating the consequences

      Each action is simulated a fixed number of times. The resulting ball position of one simulation for an action is referred to as particle. The positions of the particles are calculated according to the parameters of the action with applied standard deviations. The algorithm checks for possible collisions with the goal box and in case there are any the kick distance gets shortened appropriately. Collisions with the obstacle model are handled the same way.

      -

      Evaluation

      +

      Evaluation

      Each particle is sorted in different categories based on where on the field it is, e.g., inside the field, inside the own Goal, outside the field. If a particle lands outside the field it is sorted in the @@ -1425,7 +1418,7 @@

      Evaluation

      ground line. This is repeated for every particle of every Action that is defined. The algorithm then counts the number of particles of each action that is either inside the field or inside the opponent goal.

      -

      Decision

      +

      Decision

      If an action has less than the defined threshold of particles either inside the field or inside the opponent goal the action is discarded. For the remaining actions the one with the most particles inside the @@ -1454,7 +1447,7 @@

      Decision

      -

      Potential field

      +

      Potential field

      A potential field assigns a value to each position of the ball inside the field. The values reflect the static strategy of the game and are used to compare possible ball positions in terms of their strategic @@ -1497,7 +1490,7 @@

      Potential field

      @RCMellmannSchlotterBlum16. A continuation of this work was presented at the HUMANOIDS 2017 RoboCup Workshop @HSR-MellmannSchlotter-17.

      -

      Arm Collision Detection

      +

      Arm Collision Detection

      In the current implementation of the stand and walk motions the arms are used for stability and energy efficiency. During the stand motion the arms are kept down along the sides to minimize energy consumption, and are moved back and forth during the walk to balance the rotational forces and stabilize the walk. On the downside, these arm @@ -1540,7 +1533,7 @@

      Arm Collision Detection

      expected error which might occur with out a collision. The accuracy can be improved with a dynamic threshold. We observed a correlation between the position of the joint the average absolute error which. Thus, a threshold depending on the position of the joint might improve the results significantly.

      -

      Time synchronization

      +

      Time synchronization

      The robots communicate via wifi with the gamecontroller and with each other. The communicated information are not only used for debugging & visualization in RobotControl, their're also used in the behavior and @@ -1592,7 +1585,7 @@

      Time synchronization

      old a seen ball of its teammates really is, but future development should use the feature more and thus make the communicated information more reliable and the thereon based calculations more accurate.

      -

      IMU

      +

      IMU

      A Unscented-Kalman-Filter is used to combine the measurements of the accelerometer and gyrometer to a orientation and a gravity adjusted acceleration in the inertial frame. Quaternions are used in calculations @@ -1677,7 +1670,7 @@

      IMU

      - + diff --git a/teamreport/motion/index.html b/teamreport/motion/index.html old mode 100644 new mode 100755 index d3602d4..fc1e047 --- a/teamreport/motion/index.html +++ b/teamreport/motion/index.html @@ -780,9 +780,35 @@ + +
    • + + + + + + + + + + +
      • + + + +
    • - - Handling the Robot + + Working with logfiles + +
      • + + + + + + + + + +
    • + + Sound
      • @@ -1103,7 +1150,7 @@ -

      Motion Control

      +

      Motion Control

      The performance of a soccer robot is highly dependent on its motion ability. Together with the ability to walk, the kicking motion is one of the most important motions in a soccer game. However, at the current @@ -1131,7 +1178,7 @@ Problems arise from sensor noise and delays within the feedback loop. As a correlated project we also investigate the paradigm of local control loops, e.g. we extended the Nao with additional sensors.

      -

      Walk

      +

      Walk

      The algorithm we use to accomplish a walking motion can be subdivided into four components: the path planner, the step planner, the preview controller and stabilization.

      @@ -1154,7 +1201,7 @@

      Walk

      -

      Path Planner

      +

      Path Planner

      The path planner is intended to calculate a collision free and optimal path from the robot towards a goal. Currently that intention is not fulfilled. Instead it is used as a bridge between the behavior @@ -1180,7 +1227,7 @@

      Path Planner

      control over actual steps the path planner implements a simple mechanism to ensure that a desired step is requested until actual execution of the step, unless the step becomes obsolete in the process.

      -

      Step Planner

      +

      Step Planner

      The step planner calculates the next 2D positions of the feet based on the walk request inside the motion request.

      The walk request and the current pose of the moving foot are transformed @@ -1205,7 +1252,7 @@

      Step Planner

      Independent of the requested steps the step planner might insert zero steps for increasing the stability of the walk. A zero step is a step in which no foot is moved.

      -

      Preview Control

      +

      Preview Control

      The Preview Controller calculates the trajectory for the COM based on planed ZMPs. For estimating a stable trajectory for the COM we assume a linear inverted pendulum model with constant height. In each planning @@ -1220,7 +1267,7 @@

      Preview Control

      parameters of the preview controller and are pre-calculated. The next target COM \(x_{k+1}\) can be calculated using a linear motion model: \(\(x_{k+1} = Ax_k + ub\)\)

      -

      Stabilization

      +

      Stabilization

      The simplified model can easily be affected by disturbances in the environment. Therefore a closed loop stabilization is required.

      Different control techniques are used during step creation and execution @@ -1239,7 +1286,7 @@

      Stabilization

      the whole time. And a third controller adapts the ankles according to the current orientation of the robot's body and its change in orientation.

      -

      Energy Efficient Stand

      +

      Energy Efficient Stand

      During games we have to deal with two problems regarding the hardware of the robots. The first problem is the increasing temperature of the joints, which affects the stability of walking. The second problem is @@ -1350,7 +1397,7 @@

      Energy Efficient Stand

      - + diff --git a/teamreport/vision/index.html b/teamreport/vision/index.html old mode 100644 new mode 100755 index 30f0f38..1130980 --- a/teamreport/vision/index.html +++ b/teamreport/vision/index.html @@ -441,33 +441,6 @@ Candidate Search -- Perspective Key Points Detection - -
    • @@ -475,26 +448,6 @@ Classification - -
    • @@ -862,9 +815,35 @@ + +
    • + + + + + + + + + + +
      • + + + +
    • - - Handling the Robot + + Working with logfiles + +
      • + + + + + + + + + +
    • + + Sound
      • @@ -1185,33 +1185,6 @@ Candidate Search -- Perspective Key Points Detection - -
    • @@ -1219,26 +1192,6 @@ Classification - -
    • @@ -1267,7 +1220,7 @@ -

      Visual Perception

      +

      Visual Perception

      Visual perception is the primary way for the NAO robot of perceiving its environment. To reduce computational complexity our vision is based on a reliable field color detection. This color information is used to estimate the boundaries of the visible field region in the image. Which is done while scanning for @@ -1289,7 +1242,7 @@ arrow from $R$ to $A$ means $R$ is required by $A$. -

      Green Detection

      +

      Green Detection

      This section describes a new approach to classify the field color which has been used since late 2015. For the first time this approach has been presented in November 2015 at the RoHOW workshop in Hamburg, Germany. This constitutes the first step in the attempt for a automatic field color detection. Thereby we analyze @@ -1402,7 +1355,7 @@

      Green Detection

      shifts might be the way for a fully automatic green color classifier which would be able to cover the variety of the shades to enable a robot to play outside.

      -

      ScanLineEdgelDetector

      +

      ScanLineEdgelDetector

      @@ -1421,7 +1374,7 @@

      ScanLineEdgelDetector

      3x3-Sobel-Filters (horizontal and vertical) the orientation of the line. With the result of the field color classification we detect along every scanline a point, which marks the border of the field.

      -

      FieldDetector

      +

      FieldDetector

      With the field border points, estimated with the ScanLineEdgelDetector, we calculate for each image a polygon, which is representing the border of the field in the image.

      @@ -1433,7 +1386,7 @@

      FieldDetector

      -

      LineGraphProvider

      +

      LineGraphProvider

      This module clusters neighbouring line border points, detected by ScanLineEdgelDetector.

      @@ -1443,7 +1396,7 @@

      LineGraphProvider

      -

      RansacLineDetector

      +

      RansacLineDetector

      This module detects lines on the directed points generated by the LineGraphProvider using the Random sample consensus (RANSAC) method. Our implementation can be summarized by the following 4 steps.

      @@ -1471,7 +1424,7 @@

      RansacLineDetector

      In order to detect the remaining field lines we repeat this procedure on the remaining outliers until no lines are found.

      -

      GoalFeatureDetector

      +

      GoalFeatureDetector

      This module is the first step of the goal post detection procedure. To detect the goal posts we scan along the horizontal scan lines parallel to the artificial horizon estimated in ArtificialHorizonProvider. @@ -1496,7 +1449,7 @@

      GoalFeatureDetector

      -

      GoalDetector

      +

      GoalDetector

      The GoalDetector clusters the features found by the GoalFeatureDetector. The main idea here is, that features, which represent a goal post, must be located underneath of each other. We @@ -1535,7 +1488,7 @@

      GoalDetector

      evaluation of the candidate clusters. Although there seem to be a considerable amount of false features, both posts of the goal are detected correctly.

      -

      Black&White Ball Detection

      +

      Black&White Ball Detection

      In 2015 the standard ball used in competitions changed to a black&white foam ball as illustrated in Figure 4.13{reference-type="ref" @@ -1555,7 +1508,7 @@

      Black&White Ball Detection

      -

      Candidate Search -- Perspective Key Points Detection

      +

      Candidate Search -- Perspective Key Points Detection

      Properties of the ball as an object that can be assumed as known include

      • @@ -1603,7 +1556,7 @@

        Candidate Search -- Perspective Key Points Detection

        • -
        Representation:
        +
        Representation:

        In our case we define a ball candidate (more general a key point) as a square region in the image which is likely to contain a ball. Such a key point \(c = (x,y,r)\) can be described by its position in the image @@ -1612,7 +1565,7 @@

        Representation:
        the circle. This leads to a vast number of possible candidates in a single image and a necessity for an efficient heuristic algorithm to find the most likely ones.

        -
        Measure:
        +
        Measure:

        Intuitively described, a good key point is much brighter inside than on its outer border. For a key point \(c = (x,y,r)\) we define its intensity value \(I(c)\) by

        @@ -1627,7 +1580,7 @@
        Measure:
        images. Figure 4.17{reference-type="ref" reference="fig:bw-patch-function"} (left) illustrates the measure function for the valid pixels of the image.

        -
        Finding the local maxima:
        +
        Finding the local maxima:

        To save resources the search is performed only within the estimated field region (cf. Section 4.3{reference-type="ref" reference="s:FieldDetector"}). For a given point \(p = (i,j)\) in image we @@ -1675,7 +1628,7 @@

        Finding the local maxima:
        -

        Classification

        +

        Classification

        In the previous section we discussed how the number of possible ball candidates can be efficiently reduced. At the current state we consider about 5 candidates per image. In general these candidates look very ball @@ -1686,7 +1639,7 @@

        Classification

        collection of the sample data and give some brief remarks about our experience with the OpenCV Cascade Classifier which we used during the RoboCup competition in 2016.

        -

        Sample Data Generation

        +

        Sample Data Generation

        Collecting sample data basically involves two steps: collecting images from game situations and labeling the ones containing the ball. This can be a very tedious and time consuming task. To simplify and accelerate @@ -1713,7 +1666,7 @@

        Sample Data Generation

        -

        Classification with Convolutional Neural Networks

        +

        Classification with Convolutional Neural Networks

        In the last step the generated ball candidates (patches) are classified. At the current point we distinguish two classes: ball and noball. As a classifier we use a Convolutional Neural Network (CNN). The weights of @@ -1732,7 +1685,7 @@

        Classification with C -

        Acknowledgment

        +

        Acknowledgment

        Some of the most important parts of this ball detection procedure were inspired by very fruitful discussions with the RoboCup community. At this point we would like to thank in particular the team NaoDevils for @@ -1818,7 +1771,7 @@

        Acknowledgment

        - +