Update doc

doc/source/tutorials.rst

 Tutorials
 =========
 
-Average skyline vector homing
------------------------------
+Average place-code vector homing
+--------------------------------
 Homing with an average skyline vector consist of deriving the skyline \
 or an approximation of it from the visual information. For example, \
 ultra violet light is mostly present in the sky, and thus by summing \
@@ -21,66 +21,53 @@ vector, at the goal and current location are compared by simple difference. \
 The difference gives the homing vector, i.e. a vector proportional to \
 the velocity of the agent.
 
+Our agent needs to have a function to convert its current state to a motion. \
+This function, velocity, can be added as follow:
+
+.. literalinclude:: example/tutorials/asv_homing_grid.py
+   :lines: 12-30
+
 On a grid
 ~~~~~~~~~
+
 By restricting the agent motion on a grid, we can used a database containing \
 images rendered at pre defined location (the grid nodes).
 
 .. literalinclude:: example/tutorials/asv_homing_grid.py
-   :lines: 13
+   :lines: 35	   
 
 And initialise the senses of our virtual agent
 
 .. literalinclude:: example/tutorials/asv_homing_grid.py
-   :lines: 14
-
-The agent should calculate the average skyline location at its home location \
-i.e. the goal location during the homing task.
-
-.. literalinclude:: example/tutorials/asv_homing_grid.py
-   :lines: 17-19
-
-Our agent should have a method to calculate its velocity from the \
-current sensory information to reach its home location. The ASV homing \
-model is the method, and can be defined as follow:
-
-.. literalinclude:: example/tutorials/asv_homing_grid.py
-   :lines: 41-50
+   :lines: 36
 
 Now we have to initialise an agent moving on a grid (i.e. a GridAgent)
 
 .. literalinclude:: example/tutorials/asv_homing_grid.py
-   :lines: 24
+   :lines: 38
 
 at an initial position
 
 .. literalinclude:: example/tutorials/asv_homing_grid.py
-   :lines: 27-29
+   :lines: 40-43
 
 a mode of motion corresponding to the grid used in the database
 
 .. literalinclude:: example/tutorials/asv_homing_grid.py
-   :lines: 32-36
+   :lines: 36-50
 
-and the function to calculate the velocity, i.e. the motion of the agent
+Finally our agent is ready to fly for a number of step or until its velocity is null.
 
 .. literalinclude:: example/tutorials/asv_homing_grid.py
    :lines: 53-54
 
-Note that the position orientation and derivative (posorient_vel) is not \
-used by the function, but is required by the GridAgent.
-
-Finally our agent is ready to fly for number of step or until its velocity is null.
-
-.. literalinclude:: example/tutorials/asv_homing_grid.py
-   :lines: 56-57
-
 In close loop
 ~~~~~~~~~~~~~
 
 Catchment area of ASV
 ---------------------
 
+
 Comparing modalities
 --------------------
 

navipy/__init__.py

@@ -28,12 +28,12 @@ an agent, the Brain should have a function called velocity.
 
 For example, an stationary agent should always return a null velocity.
 
-.. literalinclude:: example/processing/apcv.py
+.. literalinclude:: example/brain/static_brain.py
    :lines: 3,7-15
 
 An agent using an average skyline homing vector, could be build as follow
 
-.. literalinclude:: example/processing/apcv.py
+.. literalinclude:: example/brain/asv_brain.py
    :lines: 3,7-34
 
 """