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doc:reasoning:overview [2015/05/29 13:18]
gkazhoya
doc:reasoning:overview [2017/08/11 15:10] (current)
gkazhoya
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 ===== Overview ===== ===== Overview =====
  
-The internal representation of the state of the world that robot lives in is called the robot'​s belief state. It includes ​the knowledge about the objects in robot'​s environment, e.g. current pose of an object ​or its dynamic state such as if it is stable or is currently in motion, and about the robot'​s internal state, such as the current configuration of its arms or its location in the world map.+The internal representation of the state of the world that robot lives in is called the robot'​s belief state. It contains ​the knowledge about the objects in robot'​s environment ​which includes the current pose of an objectits dynamic state, e.g., is it stable or is currently in motion, and knowledge ​about the robot'​s internal state, such as the current configuration of its arms or its location in the world map.
  
-CRAM uses OpenGL for visualizing the belief state, the [[http://​bulletphysics.com|Bullet physics engine]] for acquiring physics knowledge about the world and OpenGL offscrean rendering for giving the robot visual information about the world in the virtual environment.+CRAM uses [[http://​bulletphysics.com|Bullet physics engine]] ​to represent the objects in the world and for acquiring physics knowledge about them, and OpenGL ​for visualizing the belief state and for offscrean rendering for giving the robot visual information about the world in the virtual environment.
  
 The belief state representation used for geometric reasoning is built upon the representation of the world used in the Bullet physics engine. However, whereas in Bullet the objects are just a set of meshes with certain physical parameters assigned to them (such as mass, inertia etc.), in CRAM objects have additional semantic information associated with them to enable reasoning. The belief state representation used for geometric reasoning is built upon the representation of the world used in the Bullet physics engine. However, whereas in Bullet the objects are just a set of meshes with certain physical parameters assigned to them (such as mass, inertia etc.), in CRAM objects have additional semantic information associated with them to enable reasoning.
  
-Although CRAM is currently ​also used in an outdoors scenario in the scope of a [[http://​www.sherpa-project.eu/​sherpa/​|project for improving rescuing activities in avalanche scenarios]],​ the main application area of CRAM is object manipulation tasks in indoor environments,​ where the geometric reasoning engine proves to have very powerful potential. Using the geometric reasoning module of CRAM it is possible to infer:+Although CRAM has also been used in an outdoors scenario in the scope of a [[http://​www.sherpa-project.eu/​sherpa/​|project for improving rescuing activities in avalanche scenarios]],​ the main application area of CRAM is object manipulation tasks in indoor environments,​ where the geometric reasoning engine proves to have very powerful potential. Using the geometric reasoning module of CRAM it is possible to infer:
   * what is a good pose to position the robot'​s base such that the robot could be able to grasp a specific object   * what is a good pose to position the robot'​s base such that the robot could be able to grasp a specific object
   * where should the robot stand in order to be able to see a specific pose in the world, such that other objects are not in the view occluding it   * where should the robot stand in order to be able to see a specific pose in the world, such that other objects are not in the view occluding it