Adding all gifs to the doc 2 (#585)

* update docs

* update docs

* update docs

docs/index_main.rst

@@ -43,6 +43,7 @@ See this paper for more details:
    modules/arm_navigation/arm_navigation
    modules/aerial_navigation/aerial_navigation
    modules/bipedal/bipedal
+   modules/control/control
    modules/appendix/appendix
    how_to_contribute
 

docs/modules/control/control_main.rst

@@ -0,0 +1,7 @@
+.. _control:
+
+Control
+=================
+
+.. include:: inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst
+

docs/modules/control/inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst

@@ -0,0 +1,6 @@
+Inverted Pendulum MPC Control
+-----------------------------
+
+Bipedal Walking with modifying designated footsteps
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/InvertedPendulumCart/animation.gif

docs/modules/introduction_main.rst

@@ -2,4 +2,41 @@
 Introduction
 ============
 
-TBD
+TBD
+
+Definition Of Robotics
+----------------------
+
+TBD
+
+History Of Robotics
+----------------------
+
+TBD
+
+Application Of Robotics
+------------------------
+
+TBD
+
+Software for Robotics
+----------------------
+
+TBD
+
+Software for Robotics
+----------------------
+
+TBD
+
+Python for Robotics
+----------------------
+
+TBD
+
+Learning Robotics Algorithms
+----------------------------
+
+TBD
+
+

docs/modules/localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization.rst

@@ -0,0 +1,7 @@
+Ensamble Kalman Filter Localization
+-----------------------------------
+
+.. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/ensamble_kalman_filter/animation.gif
+
+This is a sensor fusion localization with Ensamble Kalman Filter(EnKF).
+

docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst

@@ -8,7 +8,6 @@ Extended Kalman Filter Localization
 
 
 .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/extended_kalman_filter/animation.gif
-   :alt: EKF
 
 This is a sensor fusion localization with Extended Kalman Filter(EKF).
 

docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst

@@ -0,0 +1,22 @@
+Histogram filter localization
+-----------------------------
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/histogram_filter/animation.gif
+
+This is a 2D localization example with Histogram filter.
+
+The red cross is true position, black points are RFID positions.
+
+The blue grid shows a position probability of histogram filter.
+
+In this simulation, x,y are unknown, yaw is known.
+
+The filter integrates speed input and range observations from RFID for
+localization.
+
+Initial position is not needed.
+
+References:
+~~~~~~~~~~~
+
+-  `PROBABILISTIC ROBOTICS`_

docs/modules/localization/localization_main.rst

@@ -3,89 +3,11 @@
 Localization
 ============
 
-.. include:: extended_kalman_filter_localization.rst
-
-
-Unscented Kalman Filter localization
-------------------------------------
-
-|2|
-
-This is a sensor fusion localization with Unscented Kalman Filter(UKF).
-
-The lines and points are same meaning of the EKF simulation.
-
-References:
-~~~~~~~~~~~
-
--  `Discriminatively Trained Unscented Kalman Filter for Mobile Robot
-   Localization`_
-
-Particle filter localization
-----------------------------
-
-|3|
-
-This is a sensor fusion localization with Particle Filter(PF).
-
-The blue line is true trajectory, the black line is dead reckoning
-trajectory,
-
-and the red line is estimated trajectory with PF.
-
-It is assumed that the robot can measure a distance from landmarks
-(RFID).
-
-This measurements are used for PF localization.
-
-How to calculate covariance matrix from particles
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The covariance matrix :math:`\Xi` from particle information is calculated by the following equation:
-
-.. math:: \Xi_{j,k}=\frac{1}{1-\sum^N_{i=1}(w^i)^2}\sum^N_{i=1}w^i(x^i_j-\mu_j)(x^i_k-\mu_k)
-
-- :math:`\Xi_{j,k}` is covariance matrix element at row :math:`i` and column :math:`k`.
-
-- :math:`w^i` is the weight of the :math:`i` th particle.
-
-- :math:`x^i_j` is the :math:`j` th state of the :math:`i` th particle.
-
-- :math:`\mu_j` is the :math:`j` th mean state of particles.
-
-References:
-~~~~~~~~~~~
-
--  `PROBABILISTIC ROBOTICS`_
--  `Improving the particle filter in high dimensions using conjugate artificial process noise`_
-
-Histogram filter localization
------------------------------
-
-|4|
-
-This is a 2D localization example with Histogram filter.
-
-The red cross is true position, black points are RFID positions.
-
-The blue grid shows a position probability of histogram filter.
-
-In this simulation, x,y are unknown, yaw is known.
-
-The filter integrates speed input and range observations from RFID for
-localization.
-
-Initial position is not needed.
-
-References:
-~~~~~~~~~~~
-
--  `PROBABILISTIC ROBOTICS`_
+.. include:: extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst
+.. include:: ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization.rst
+.. include:: unscented_kalman_filter_localization/unscented_kalman_filter_localization.rst
+.. include:: histogram_filter_localization/histogram_filter_localization.rst
+.. include:: particle_filter_localization/particle_filter_localization.rst
 
 .. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/
-.. _Discriminatively Trained Unscented Kalman Filter for Mobile Robot Localization: https://www.researchgate.net/publication/267963417_Discriminatively_Trained_Unscented_Kalman_Filter_for_Mobile_Robot_Localization
-.. _Improving the particle filter in high dimensions using conjugate artificial process noise: https://arxiv.org/pdf/1801.07000.pdf
 
-.. |2| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/unscented_kalman_filter/animation.gif
-.. |3| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/particle_filter/animation.gif
-.. |4| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/histogram_filter/animation.gif

docs/modules/localization/particle_filter_localization/particle_filter_localization.rst

@@ -0,0 +1,37 @@
+Particle filter localization
+----------------------------
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/particle_filter/animation.gif
+
+This is a sensor fusion localization with Particle Filter(PF).
+
+The blue line is true trajectory, the black line is dead reckoning
+trajectory,
+
+and the red line is estimated trajectory with PF.
+
+It is assumed that the robot can measure a distance from landmarks
+(RFID).
+
+This measurements are used for PF localization.
+
+How to calculate covariance matrix from particles
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The covariance matrix :math:`\Xi` from particle information is calculated by the following equation:
+
+.. math:: \Xi_{j,k}=\frac{1}{1-\sum^N_{i=1}(w^i)^2}\sum^N_{i=1}w^i(x^i_j-\mu_j)(x^i_k-\mu_k)
+
+- :math:`\Xi_{j,k}` is covariance matrix element at row :math:`i` and column :math:`k`.
+
+- :math:`w^i` is the weight of the :math:`i` th particle.
+
+- :math:`x^i_j` is the :math:`j` th state of the :math:`i` th particle.
+
+- :math:`\mu_j` is the :math:`j` th mean state of particles.
+
+References:
+~~~~~~~~~~~
+
+-  `PROBABILISTIC ROBOTICS`_
+- `Improving the particle filter in high dimensions using conjugate artificial process noise <https://arxiv.org/pdf/1801.07000.pdf>`_

docs/modules/localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization.rst

@@ -0,0 +1,13 @@
+Unscented Kalman Filter localization
+------------------------------------
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/unscented_kalman_filter/animation.gif
+
+This is a sensor fusion localization with Unscented Kalman Filter(UKF).
+
+The lines and points are same meaning of the EKF simulation.
+
+References:
+~~~~~~~~~~~
+
+- `Discriminatively Trained Unscented Kalman Filter for Mobile Robot Localization <https://www.researchgate.net/publication/267963417_Discriminatively_Trained_Unscented_Kalman_Filter_for_Mobile_Robot_Localization>`_

docs/modules/mapping/circle_fitting/circle_fitting.rst

@@ -0,0 +1,13 @@
+Object shape recognition using circle fitting
+---------------------------------------------
+
+This is an object shape recognition using circle fitting.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/circle_fitting/animation.gif
+
+The blue circle is the true object shape.
+
+The red crosses are observations from a ranging sensor.
+
+The red circle is the estimated object shape using circle fitting.
+

docs/modules/mapping/gaussian_grid_map/gaussian_grid_map.rst

@@ -0,0 +1,6 @@
+Gaussian grid map
+-----------------
+
+This is a 2D Gaussian grid mapping example.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/gaussian_grid_map/animation.gif

docs/modules/mapping/k_means_object_clustering/k_means_object_clustering.rst

@@ -0,0 +1,6 @@
+k-means object clustering
+-------------------------
+
+This is a 2D object clustering with k-means algorithm.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/kmeans_clustering/animation.gif

docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial.rst

@@ -1,3 +1,7 @@
+Lidar to grid map
+--------------------
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/lidar_to_grid_map/animation.gif
 
 This simple tutorial shows how to read LIDAR (range) measurements from a
 file and convert it to occupancy grid.
@@ -12,8 +16,7 @@ a ``numpy array``, and numbers close to 1 means the cell is occupied
 free (*marked with green*). The grid has the ability to represent
 unknown (unobserved) areas, which are close to 0.5.
 
-.. figure:: lidar_to_grid_map_tutorial_files/grid_map_example.png
-   :alt: Example
+.. figure:: lidar_to_grid_map_tutorial/grid_map_example.png
 
 In order to construct the grid map from the measurement we need to
 discretise the values. But, first let’s need to ``import`` some
@@ -65,7 +68,7 @@ From the distances and the angles it is easy to determine the ``x`` and
 
 
 
-.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_5_0.png
+.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_5_0.png
 
 
 The ``lidar_to_grid_map.py`` contains handy functions which can used to
@@ -86,7 +89,7 @@ map. Let’s see how this works.
 
 
 
-.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_7_0.png
+.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_7_0.png
 
 
 .. code:: ipython3
@@ -103,7 +106,7 @@ map. Let’s see how this works.
 
 
 
-.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_8_0.png
+.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_8_0.png
 
 
 To fill empty areas, a queue-based algorithm can be used that can be
@@ -160,7 +163,7 @@ from a center point (e.g. (10, 20)) with zeros:
 
 
 
-.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_12_0.png
+.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_12_0.png
 
 
 Let’s use this flood fill on real data:
@@ -191,5 +194,5 @@ Let’s use this flood fill on real data:
 
 
 
-.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_14_1.png
+.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_14_1.png
 

docs/modules/mapping/mapping_main.rst

@@ -3,49 +3,10 @@
 Mapping
 =======
 
-Gaussian grid map
------------------
+.. include:: gaussian_grid_map/gaussian_grid_map.rst
+.. include:: ray_casting_grid_map/ray_casting_grid_map.rst
+.. include:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial.rst
+.. include:: k_means_object_clustering/k_means_object_clustering.rst
+.. include:: circle_fitting/circle_fitting.rst
+.. include:: rectangle_fitting/rectangle_fitting.rst
 
-This is a 2D Gaussian grid mapping example.
-
-|2|
-
-Ray casting grid map
---------------------
-
-This is a 2D ray casting grid mapping example.
-
-|3|
-
-Lidar to grid map
---------------------
-
-|6|
-
-.. include:: lidar_to_grid_map_tutorial.rst
-
-k-means object clustering
--------------------------
-
-This is a 2D object clustering with k-means algorithm.
-
-|4|
-
-Object shape recognition using circle fitting
----------------------------------------------
-
-This is an object shape recognition using circle fitting.
-
-|5|
-
-The blue circle is the true object shape.
-
-The red crosses are observations from a ranging sensor.
-
-The red circle is the estimated object shape using circle fitting.
-
-.. |2| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/gaussian_grid_map/animation.gif
-.. |3| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/raycasting_grid_map/animation.gif
-.. |4| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/kmeans_clustering/animation.gif
-.. |5| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/circle_fitting/animation.gif
-.. |6| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/lidar_to_grid_map/animation.gif

docs/modules/mapping/ray_casting_grid_map/ray_casting_grid_map.rst

@@ -0,0 +1,6 @@
+Ray casting grid map
+--------------------
+
+This is a 2D ray casting grid mapping example.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/raycasting_grid_map/animation.gif

docs/modules/mapping/rectangle_fitting/rectangle_fitting.rst

@@ -0,0 +1,7 @@
+Object shape recognition using rectangle fitting
+------------------------------------------------
+
+This is an object shape recognition using rectangle fitting.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/rectangle_fitting/animation.gif
+