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doc:reasoning_about_logged_experiences [2014/02/19 08:21] – admindoc:reasoning_about_logged_experiences [2014/06/05 17:44] – [Reasoning over logged data using knowrob_mongo] admin
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 ====== Reasoning about logged robot experiences ====== ====== Reasoning about logged robot experiences ======
  
 +This page lists methods and tools for reasoning about log data of robot tasks created using the [[http://www.cram-system.org/doc#logging_infrastructure|CRAM logging infrastructure]]. These logs consist of two parts: High-level plan events are stored as action instances in an OWL file, while lower-level high-volume data is logged separately into a MongoDB database. This allows efficient recording and storage of data like tf poses, perception results etc. Special Prolog predicates allow to access the information stored in the database.
  
-====== Symbolic reasoning about logged data ======+===== Installation =====
  
-This page lists different queries related to the sybolic inference about logged data stored in a MongoDB database.+Usually, the log files are created using the [[http://www.cram-system.org/doc/logging/roslog|CRAM logging infrastructure]] that also includes the installation of a MongoDB database. If you would like to use the system on another computer, make sure to have a MongoDB database installed (the Ubuntu default version should work). 
 +  sudo apt-get install mongodb-dev
  
-===== Startup =====+The logged data is stored in a database called 'roslog'; the collections in this database correspond to the respective topic names, for example 'tf'. From the KnowRob side, you will need the "Complete system including add-on packages" installation of KnowRob from source and compile mod_execution_trace using  
 +  rosmake mod_execution_trace 
 +   
 +We have prepared an archive with test data logged during a pick-and-place task with the PR2 robot. You can download the file from [[http://knowrob.org/files/pick-and-place.tar.bz2|here]] and insert it into your MongoDB using the following commands: 
 +  tar -xvjf pick-and-place.tar.bz2 
 +  cd pick-and-place 
 +  mongoimport --db roslog --collection tf tf.json 
 +  mongoimport --db roslog --collection logged_designators logged_designators.json 
 +  mongoimport --db roslog --collection logged_metadata logged_metadata 
 + 
 + 
 +===== Reasoning about logged data using knowrob_mongo ===== 
 +The knowrob_mongo package contains methods for accessing information in the MongoDB database via KnowRob predicates, for example the logged tf pose data or perception results. 
 + 
 +==== Startup ====
  
   $ rosrun rosprolog rosprolog knowrob_mongo   $ rosrun rosprolog rosprolog knowrob_mongo
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   ?- owl_parse('owl/perceive-no-details.owl', false, false, true).   ?- owl_parse('owl/perceive-no-details.owl', false, false, true).
  
-===== Queries =====+==== Queries ====
   # read pose of cameras in /map coordinates at the beginning of event_dInF6Fvz   # read pose of cameras in /map coordinates at the beginning of event_dInF6Fvz
   ?- owl_individual_of(A, srdl2comp:'Camera'),    ?- owl_individual_of(A, srdl2comp:'Camera'), 
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-====== Reasoning over plan logs using mod_execution_trace ======+===== Reasoning over plan logs using mod_execution_trace =====
  
-===== Prerequisites =====+==== Prerequisites ====
 1) Get latest version of knowrob and knowrob_addons from [[https://github.com/knowrob/knowrob | Link 1]] [[https://github.com/knowrob/knowrob_addons | Link 2]] respectively. Put these ROS stacks into your ROS workspace. 1) Get latest version of knowrob and knowrob_addons from [[https://github.com/knowrob/knowrob | Link 1]] [[https://github.com/knowrob/knowrob_addons | Link 2]] respectively. Put these ROS stacks into your ROS workspace.
  
 2) Get the dataset from [[https://www.dropbox.com/s/9w4wsl2u6h9j694/packaging2.zip | Link]] (Note that dataset is slightly modified version of Jan's. Namely, some modifications were done in namespaces and class namings). Extract it somewhere you choose. 2) Get the dataset from [[https://www.dropbox.com/s/9w4wsl2u6h9j694/packaging2.zip | Link]] (Note that dataset is slightly modified version of Jan's. Namely, some modifications were done in namespaces and class namings). Extract it somewhere you choose.
  
-===== Starting up the Software =====+==== Starting up the Software ====
  
   $ rosrun rosprolog rosprolog mod_vis   $ rosrun rosprolog rosprolog mod_vis
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   ?- owl_parse('path_of_dataset_should_be_here/perceive-no-details.owl', false, false, true).   ?- owl_parse('path_of_dataset_should_be_here/perceive-no-details.owl', false, false, true).
      
-===== Example Query =====+==== Example Query ====
 "What is the probability of a certain task was failed during the execution of a plan?" is shown as an example. It is also one of the use-cases what we want to show in our ACS'13 paper. "What is the probability of a certain task was failed during the execution of a plan?" is shown as an example. It is also one of the use-cases what we want to show in our ACS'13 paper.