From 3e0cae2a691f7d6997b78fd75d8df5fd0064ef23 Mon Sep 17 00:00:00 2001
From: "Olivier J.N. Bertrand" <olivier.bertrand@uni-bielefeld.de>
Date: Wed, 7 Feb 2018 17:00:01 +0100
Subject: [PATCH] Update doc
---
doc/source/tutorials.rst | 45 ++++++++++++++--------------------------
navipy/__init__.py | 4 ++--
2 files changed, 18 insertions(+), 31 deletions(-)
diff --git a/doc/source/tutorials.rst b/doc/source/tutorials.rst
index 5cec029..da5d1ca 100644
--- a/doc/source/tutorials.rst
+++ b/doc/source/tutorials.rst
@@ -1,8 +1,8 @@
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
--------------------
diff --git a/navipy/__init__.py b/navipy/__init__.py
index 3e7156c..11aa540 100644
--- a/navipy/__init__.py
+++ b/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
"""
--
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