update docs (#589)

* update docs

* update docs

docs/modules/path_planning/bezier_path/bezier_path.rst

@@ -0,0 +1,20 @@
+Bezier path planning
+--------------------
+
+A sample code of Bezier path planning.
+
+It is based on 4 control points Beizer path.
+
+.. image:: Bezier_path/Figure_1.png
+
+If you change the offset distance from start and end point,
+
+You can get different Beizer course:
+
+.. image:: Bezier_path/Figure_2.png
+
+Ref:
+
+-  `Continuous Curvature Path Generation Based on Bezier Curves for
+   Autonomous
+   Vehicles <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.294.6438&rep=rep1&type=pdf>`__

docs/modules/path_planning/bspline_path/bspline_path.rst

@@ -0,0 +1,14 @@
+B-Spline planning
+-----------------
+
+.. image:: bspline_path/Figure_1.png
+
+This is a path planning with B-Spline curse.
+
+If you input waypoints, it generates a smooth path with B-Spline curve.
+
+The final course should be on the first and last waypoints.
+
+Ref:
+
+-  `B-spline - Wikipedia <https://en.wikipedia.org/wiki/B-spline>`__

docs/modules/path_planning/bugplanner/bugplanner.rst

@@ -0,0 +1,8 @@
+Bug planner
+-----------
+
+This is a 2D planning with Bug algorithm.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BugPlanner/animation.gif
+
+- `ECE452 Bug Algorithms <https://sites.google.com/site/ece452bugalgorithms/>`_

docs/modules/path_planning/coverage_path/coverage_path.rst

@@ -0,0 +1,34 @@
+Coverage path planner
+---------------------
+
+Grid based sweep
+~~~~~~~~~~~~~~~~
+
+This is a 2D grid based sweep coverage path planner simulation:
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/GridBasedSweepCPP/animation.gif
+
+Spiral Spanning Tree
+~~~~~~~~~~~~~~~~~~~~
+
+This is a 2D grid based spiral spanning tree coverage path planner simulation:
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/SpiralSpanningTreeCPP/animation1.gif
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/SpiralSpanningTreeCPP/animation2.gif
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/SpiralSpanningTreeCPP/animation3.gif
+
+- `Spiral-STC: An On-Line Coverage Algorithm of Grid Environments by a Mobile Robot <https://ieeexplore.ieee.org/abstract/document/1013479>`_
+
+
+Wavefront path
+~~~~~~~~~~~~~~
+
+This is a 2D grid based wavefront coverage path planner simulation:
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/WavefrontCPP/animation1.gif
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/WavefrontCPP/animation2.gif
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/WavefrontCPP/animation3.gif
+
+- `Planning paths of complete coverage of an unstructured environment by a mobile robot <http://pinkwink.kr/attachment/cfile3.uf@1354654A4E8945BD13FE77.pdf>`_
+
+

docs/modules/path_planning/cubic_spline/cubic_spline.rst

@@ -0,0 +1,13 @@
+Cubic spline planning
+---------------------
+
+A sample code for cubic path planning.
+
+This code generates a curvature continuous path based on x-y waypoints
+with cubic spline.
+
+Heading angle of each point can be also calculated analytically.
+
+.. image:: cubic_spline/Figure_1.png
+.. image:: cubic_spline/Figure_2.png
+.. image:: cubic_spline/Figure_3.png

docs/modules/path_planning/dubins_path/dubins_path.rst

@@ -0,0 +1,11 @@
+Dubins path planning
+--------------------
+
+A sample code for Dubins path planning.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DubinsPath/animation.gif?raw=True
+
+Ref:
+
+-  `Dubins path -
+   Wikipedia <https://en.wikipedia.org/wiki/Dubins_path>`__

docs/modules/path_planning/dynamic_window_approach/dynamic_window_approach.rst

@@ -0,0 +1,9 @@
+Dynamic Window Approach
+-----------------------
+
+This is a 2D navigation sample code with Dynamic Window Approach.
+
+-  `The Dynamic Window Approach to Collision
+   Avoidance <https://www.ri.cmu.edu/pub_files/pub1/fox_dieter_1997_1/fox_dieter_1997_1.pdf>`__
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DynamicWindowApproach/animation.gif

docs/modules/path_planning/eta3_spline/eta3_spline.rst

@@ -0,0 +1,13 @@
+.. _eta^3-spline-path-planning:
+
+Eta^3 Spline path planning
+--------------------------
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Eta3SplinePath/animation.gif
+
+This is a path planning with Eta^3 spline.
+
+Ref:
+
+-  `\\eta^3-Splines for the Smooth Path Generation of Wheeled Mobile
+   Robots <https://ieeexplore.ieee.org/document/4339545/>`__

docs/modules/path_planning/frenet_frame_path/frenet_frame_path.rst

@@ -0,0 +1,20 @@
+Optimal Trajectory in a Frenet Frame
+------------------------------------
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/animation.gif
+
+This is optimal trajectory generation in a Frenet Frame.
+
+The cyan line is the target course and black crosses are obstacles.
+
+The red line is predicted path.
+
+Ref:
+
+-  `Optimal Trajectory Generation for Dynamic Street Scenarios in a
+   Frenet
+   Frame <https://www.researchgate.net/profile/Moritz_Werling/publication/224156269_Optimal_Trajectory_Generation_for_Dynamic_Street_Scenarios_in_a_Frenet_Frame/links/54f749df0cf210398e9277af.pdf>`__
+
+-  `Optimal trajectory generation for dynamic street scenarios in a
+   Frenet Frame <https://www.youtube.com/watch?v=Cj6tAQe7UCY>`__
+

docs/modules/path_planning/grid_base_search/grid_base_search.rst

@@ -0,0 +1,93 @@
+Grid based search
+-----------------
+
+Breadth First Search
+~~~~~~~~~~~~~~~~~~~~
+
+This is a 2D grid based path planning with Breadth first search algorithm.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BreadthFirstSearch/animation.gif
+
+In the animation, cyan points are searched nodes.
+
+Depth First Search
+~~~~~~~~~~~~~~~~~~~~
+
+This is a 2D grid based path planning with Depth first search algorithm.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DepthFirstSearch/animation.gif
+
+In the animation, cyan points are searched nodes.
+
+Dijkstra algorithm
+~~~~~~~~~~~~~~~~~~
+
+This is a 2D grid based shortest path planning with Dijkstra's algorithm.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Dijkstra/animation.gif
+
+In the animation, cyan points are searched nodes.
+
+.. _a*-algorithm:
+
+A\* algorithm
+~~~~~~~~~~~~~
+
+This is a 2D grid based shortest path planning with A star algorithm.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/AStar/animation.gif
+
+In the animation, cyan points are searched nodes.
+
+Its heuristic is 2D Euclid distance.
+
+Bidirectional A\* algorithm
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+This is a 2D grid based shortest path planning with bidirectional A star algorithm.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BidirectionalAStar/animation.gif
+
+In the animation, cyan points are searched nodes.
+
+.. _D*-algorithm:
+
+D\* algorithm
+~~~~~~~~~~~~~
+
+This is a 2D grid based shortest path planning with D star algorithm.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DStar/animation.gif
+
+The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm.
+
+Ref:
+
+-  `D* search Wikipedia <https://en.wikipedia.org/wiki/D*>`__
+
+D\* lite algorithm
+~~~~~~~~~~~~~~~~~~
+
+This is a 2D grid based path planning and replanning with D star lite algorithm.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DStarLite/animation.gif
+
+Ref:
+
+- `Improved Fast Replanning for Robot Navigation in Unknown Terrain <http://www.cs.cmu.edu/~maxim/files/dlite_icra02.pdf>`_
+
+
+Potential Field algorithm
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+This is a 2D grid based path planning with Potential Field algorithm.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/PotentialFieldPlanning/animation.gif
+
+In the animation, the blue heat map shows potential value on each grid.
+
+Ref:
+
+-  `Robotic Motion Planning:Potential
+   Functions <https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf>`__
+

docs/modules/path_planning/hybridastar/hybridastar.rst

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+Hybrid a star
+---------------------
+
+This is a simple vehicle model based hybrid A\* path planner.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/HybridAStar/animation.gif

docs/modules/path_planning/lqr_path/lqr_path.rst

@@ -0,0 +1,6 @@
+LQR based path planning
+-----------------------
+
+A sample code using LQR based path planning for double integrator model.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRPlanner/animation.gif?raw=true

docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator.rst

@@ -0,0 +1,22 @@
+Model Predictive Trajectory Generator
+-------------------------------------
+
+This is a path optimization sample on model predictive trajectory
+generator.
+
+This algorithm is used for state lattice planner.
+
+Path optimization sample
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ModelPredictiveTrajectoryGenerator/kn05animation.gif
+
+Lookup table generation sample
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: model_predictive_trajectory_generator/lookup_table.png
+
+Ref:
+
+-  `Optimal rough terrain trajectory generation for wheeled mobile
+   robots <http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__

docs/modules/path_planning/path_planning_main.rst

@@ -3,410 +3,22 @@
 Path Planning
 =============
 
-Dynamic Window Approach
------------------------
-
-This is a 2D navigation sample code with Dynamic Window Approach.
-
--  `The Dynamic Window Approach to Collision
-   Avoidance <https://www.ri.cmu.edu/pub_files/pub1/fox_dieter_1997_1/fox_dieter_1997_1.pdf>`__
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DynamicWindowApproach/animation.gif
-
-Grid based search
------------------
-
-Dijkstra algorithm
-~~~~~~~~~~~~~~~~~~
-
-This is a 2D grid based shortest path planning with Dijkstra's
-algorithm.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Dijkstra/animation.gif
-
-In the animation, cyan points are searched nodes.
-
-.. _a*-algorithm:
-
-A\* algorithm
-~~~~~~~~~~~~~
-
-This is a 2D grid based shortest path planning with A star algorithm.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/AStar/animation.gif
-
-In the animation, cyan points are searched nodes.
-
-Its heuristic is 2D Euclid distance.
-
-.. _D*-algorithm:
-
-D\* algorithm
-~~~~~~~~~~~~~
-
-This is a 2D grid based shortest path planning with D star algorithm.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DStar/animation.gif
-
-The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm.
-
-Ref:
-
--  `D* search Wikipedia <https://en.wikipedia.org/wiki/D*>`__
-
-Potential Field algorithm
-~~~~~~~~~~~~~~~~~~~~~~~~~
-
-This is a 2D grid based path planning with Potential Field algorithm.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/PotentialFieldPlanning/animation.gif
-
-In the animation, the blue heat map shows potential value on each grid.
-
-Ref:
-
--  `Robotic Motion Planning:Potential
-   Functions <https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf>`__
-
-Model Predictive Trajectory Generator
--------------------------------------
-
-This is a path optimization sample on model predictive trajectory
-generator.
-
-This algorithm is used for state lattice planner.
-
-Path optimization sample
-~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ModelPredictiveTrajectoryGenerator/kn05animation.gif
-
-Lookup table generation sample
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. image:: model_predictive_trajectry_generator/lookuptable.png
-
-Ref:
-
--  `Optimal rough terrain trajectory generation for wheeled mobile
-   robots <http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
-
-
-State Lattice Planning
-----------------------
-
-This script is a path planning code with state lattice planning.
-
-This code uses the model predictive trajectory generator to solve
-boundary problem.
-
-Ref:
-
--  `Optimal rough terrain trajectory generation for wheeled mobile
-   robots <http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
-
--  `State Space Sampling of Feasible Motions for High-Performance Mobile
-   Robot Navigation in Complex
-   Environments <http://www.frc.ri.cmu.edu/~alonzo/pubs/papers/JFR_08_SS_Sampling.pdf>`__
-
-Uniform polar sampling
-~~~~~~~~~~~~~~~~~~~~~~
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/UniformPolarSampling.gif
-
-Biased polar sampling
-~~~~~~~~~~~~~~~~~~~~~
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/BiasedPolarSampling.gif
-
-Lane sampling
-~~~~~~~~~~~~~
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/LaneSampling.gif
-
-.. _probabilistic-road-map-(prm)-planning:
-
-Probabilistic Road-Map (PRM) planning
--------------------------------------
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ProbabilisticRoadMap/animation.gif
-
-This PRM planner uses Dijkstra method for graph search.
-
-In the animation, blue points are sampled points,
-
-Cyan crosses means searched points with Dijkstra method,
-
-The red line is the final path of PRM.
-
-Ref:
-
--  `Probabilistic roadmap -
-   Wikipedia <https://en.wikipedia.org/wiki/Probabilistic_roadmap>`__
-
-　　
-
-Voronoi Road-Map planning
--------------------------
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/VoronoiRoadMap/animation.gif
-
-This Voronoi road-map planner uses Dijkstra method for graph search.
-
-In the animation, blue points are Voronoi points,
-
-Cyan crosses mean searched points with Dijkstra method,
-
-The red line is the final path of Vornoi Road-Map.
-
-Ref:
-
--  `Robotic Motion
-   Planning <https://www.cs.cmu.edu/~motionplanning/lecture/Chap5-RoadMap-Methods_howie.pdf>`__
-
-.. _rapidly-exploring-random-trees-(rrt):
-
-Rapidly-Exploring Random Trees (RRT)
-------------------------------------
-
-Basic RRT
-~~~~~~~~~
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRT/animation.gif
-
-This is a simple path planning code with Rapidly-Exploring Random Trees
-(RRT)
-
-Black circles are obstacles, green line is a searched tree, red crosses
-are start and goal positions.
-
-.. include:: rrt_star.rst
-
-
-RRT with dubins path
-~~~~~~~~~~~~~~~~~~~~
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTDubins/animation.gif
-
-Path planning for a car robot with RRT and dubins path planner.
-
-.. _rrt*-with-dubins-path:
-
-RRT\* with dubins path
-~~~~~~~~~~~~~~~~~~~~~~
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarDubins/animation.gif
-
-Path planning for a car robot with RRT\* and dubins path planner.
-
-.. _rrt*-with-reeds-sheep-path:
-
-RRT\* with reeds-sheep path
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarReedsShepp/animation.gif
-
-Path planning for a car robot with RRT\* and reeds sheep path planner.
-
-.. _informed-rrt*:
-
-Informed RRT\*
-~~~~~~~~~~~~~~
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/InformedRRTStar/animation.gif
-
-This is a path planning code with Informed RRT*.
-
-The cyan ellipse is the heuristic sampling domain of Informed RRT*.
-
-Ref:
-
--  `Informed RRT\*: Optimal Sampling-based Path Planning Focused via
-   Direct Sampling of an Admissible Ellipsoidal
-   Heuristic <https://arxiv.org/pdf/1404.2334.pdf>`__
-
-.. _batch-informed-rrt*:
-
-Batch Informed RRT\*
-~~~~~~~~~~~~~~~~~~~~
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BatchInformedRRTStar/animation.gif
-
-This is a path planning code with Batch Informed RRT*.
-
-Ref:
-
--  `Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the
-   Heuristically Guided Search of Implicit Random Geometric
-   Graphs <https://arxiv.org/abs/1405.5848>`__
-
-.. _closed-loop-rrt*:
-
-Closed Loop RRT\*
-~~~~~~~~~~~~~~~~~
-
-A vehicle model based path planning with closed loop RRT*.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ClosedLoopRRTStar/animation.gif
-
-In this code, pure-pursuit algorithm is used for steering control,
-
-PID is used for speed control.
-
-Ref:
-
--  `Motion Planning in Complex Environments using Closed-loop
-   Prediction <http://acl.mit.edu/papers/KuwataGNC08.pdf>`__
-
--  `Real-time Motion Planning with Applications to Autonomous Urban
-   Driving <http://acl.mit.edu/papers/KuwataTCST09.pdf>`__
-
--  `[1601.06326] Sampling-based Algorithms for Optimal Motion Planning
-   Using Closed-loop Prediction <https://arxiv.org/abs/1601.06326>`__
-
-.. _lqr-rrt*:
-
-LQR-RRT\*
-~~~~~~~~~
-
-This is a path planning simulation with LQR-RRT*.
-
-A double integrator motion model is used for LQR local planner.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRRRTStar/animation.gif
-
-Ref:
-
--  `LQR-RRT\*: Optimal Sampling-Based Motion Planning with Automatically
-   Derived Extension
-   Heuristics <http://lis.csail.mit.edu/pubs/perez-icra12.pdf>`__
-
--  `MahanFathi/LQR-RRTstar: LQR-RRT\* method is used for random motion
-   planning of a simple pendulum in its phase
-   plot <https://github.com/MahanFathi/LQR-RRTstar>`__
-
-Cubic spline planning
----------------------
-
-A sample code for cubic path planning.
-
-This code generates a curvature continuous path based on x-y waypoints
-with cubic spline.
-
-Heading angle of each point can be also calculated analytically.
-
-.. image:: cubic_spline/Figure_1.png
-.. image:: cubic_spline/Figure_2.png
-.. image:: cubic_spline/Figure_3.png
-
-B-Spline planning
------------------
-
-.. image:: bspline_path/Figure_1.png
-
-This is a path planning with B-Spline curse.
-
-If you input waypoints, it generates a smooth path with B-Spline curve.
-
-The final course should be on the first and last waypoints.
-
-Ref:
-
--  `B-spline - Wikipedia <https://en.wikipedia.org/wiki/B-spline>`__
-
-.. _eta^3-spline-path-planning:
-
-Eta^3 Spline path planning
---------------------------
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Eta3SplinePath/animation.gif
-
-This is a path planning with Eta^3 spline.
-
-Ref:
-
--  `\\eta^3-Splines for the Smooth Path Generation of Wheeled Mobile
-   Robots <https://ieeexplore.ieee.org/document/4339545/>`__
-
-Bezier path planning
---------------------
-
-A sample code of Bezier path planning.
-
-It is based on 4 control points Beier path.
-
-.. image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/docs/modules/path_planning/Bezier_path_planning/Figure_1.png?raw=True
-
-If you change the offset distance from start and end point,
-
-You can get different Beizer course:
-
-.. image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/docs/modules/path_planning/Bezier_path_planning/Figure_2.png?raw=True
-
-Ref:
-
--  `Continuous Curvature Path Generation Based on Bezier Curves for
-   Autonomous
-   Vehicles <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.294.6438&rep=rep1&type=pdf>`__
-
-
-.. include:: quintic_polynomials_planner.rst
-
-
-Dubins path planning
---------------------
-
-A sample code for Dubins path planning.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DubinsPath/animation.gif?raw=True
-
-Ref:
-
--  `Dubins path -
-   Wikipedia <https://en.wikipedia.org/wiki/Dubins_path>`__
-
-Reeds Shepp planning
---------------------
-
-A sample code with Reeds Shepp path planning.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ReedsSheppPath/animation.gif?raw=true
-
-Ref:
-
--  `15.3.2 Reeds-Shepp
-   Curves <http://planning.cs.uiuc.edu/node822.html>`__
-
--  `optimal paths for a car that goes both forwards and
-   backwards <https://pdfs.semanticscholar.org/932e/c495b1d0018fd59dee12a0bf74434fac7af4.pdf>`__
-
--  `ghliu/pyReedsShepp: Implementation of Reeds Shepp
-   curve. <https://github.com/ghliu/pyReedsShepp>`__
-
-LQR based path planning
------------------------
-
-A sample code using LQR based path planning for double integrator model.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRPlanner/animation.gif?raw=true
-
-Optimal Trajectory in a Frenet Frame
-------------------------------------
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/animation.gif
-
-This is optimal trajectory generation in a Frenet Frame.
-
-The cyan line is the target course and black crosses are obstacles.
-
-The red line is predicted path.
-
-Ref:
-
--  `Optimal Trajectory Generation for Dynamic Street Scenarios in a
-   Frenet
-   Frame <https://www.researchgate.net/profile/Moritz_Werling/publication/224156269_Optimal_Trajectory_Generation_for_Dynamic_Street_Scenarios_in_a_Frenet_Frame/links/54f749df0cf210398e9277af.pdf>`__
-
--  `Optimal trajectory generation for dynamic street scenarios in a
-   Frenet Frame <https://www.youtube.com/watch?v=Cj6tAQe7UCY>`__
-
+.. include:: dynamic_window_approach/dynamic_window_approach.rst
+.. include:: bugplanner/bugplanner.rst
+.. include:: grid_base_search/grid_base_search.rst
+.. include:: model_predictive_trajectory_generator/model_predictive_trajectory_generator.rst
+.. include:: state_lattice_planner/state_lattice_planner.rst
+.. include:: prm_planner/prm_planner.rst
+.. include:: vrm_planner/vrm_planner.rst
+.. include:: rrt/rrt.rst
+.. include:: cubic_spline/cubic_spline.rst
+.. include:: bspline_path/bspline_path.rst
+.. include:: eta3_spline/eta3_spline.rst
+.. include:: bezier_path/bezier_path.rst
+.. include:: quintic_polynomials_planner/quintic_polynomials_planner.rst
+.. include:: dubins_path/dubins_path.rst
+.. include:: reeds_shepp_path/reeds_shepp_path.rst
+.. include:: lqr_path/lqr_path.rst
+.. include:: hybridastar/hybridastar.rst
+.. include:: frenet_frame_path/frenet_frame_path.rst
+.. include:: coverage_path/coverage_path.rst

docs/modules/path_planning/prm_planner/prm_planner.rst

@@ -0,0 +1,19 @@
+.. _probabilistic-road-map-(prm)-planning:
+
+Probabilistic Road-Map (PRM) planning
+-------------------------------------
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ProbabilisticRoadMap/animation.gif
+
+This PRM planner uses Dijkstra method for graph search.
+
+In the animation, blue points are sampled points,
+
+Cyan crosses means searched points with Dijkstra method,
+
+The red line is the final path of PRM.
+
+Ref:
+
+-  `Probabilistic roadmap -
+   Wikipedia <https://en.wikipedia.org/wiki/Probabilistic_roadmap>`__

docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path.rst

@@ -0,0 +1,17 @@
+Reeds Shepp planning
+--------------------
+
+A sample code with Reeds Shepp path planning.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ReedsSheppPath/animation.gif?raw=true
+
+Ref:
+
+-  `15.3.2 Reeds-Shepp
+   Curves <http://planning.cs.uiuc.edu/node822.html>`__
+
+-  `optimal paths for a car that goes both forwards and
+   backwards <https://pdfs.semanticscholar.org/932e/c495b1d0018fd59dee12a0bf74434fac7af4.pdf>`__
+
+-  `ghliu/pyReedsShepp: Implementation of Reeds Shepp
+   curve. <https://github.com/ghliu/pyReedsShepp>`__

docs/modules/path_planning/rrt/rrt.rst

@@ -0,0 +1,118 @@
+.. _rapidly-exploring-random-trees-(rrt):
+
+Rapidly-Exploring Random Trees (RRT)
+------------------------------------
+
+Basic RRT
+~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRT/animation.gif
+
+This is a simple path planning code with Rapidly-Exploring Random Trees
+(RRT)
+
+Black circles are obstacles, green line is a searched tree, red crosses
+are start and goal positions.
+
+.. include:: rrt/rrt_star.rst
+
+
+RRT with dubins path
+~~~~~~~~~~~~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTDubins/animation.gif
+
+Path planning for a car robot with RRT and dubins path planner.
+
+.. _rrt*-with-dubins-path:
+
+RRT\* with dubins path
+~~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarDubins/animation.gif
+
+Path planning for a car robot with RRT\* and dubins path planner.
+
+.. _rrt*-with-reeds-sheep-path:
+
+RRT\* with reeds-sheep path
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarReedsShepp/animation.gif
+
+Path planning for a car robot with RRT\* and reeds sheep path planner.
+
+.. _informed-rrt*:
+
+Informed RRT\*
+~~~~~~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/InformedRRTStar/animation.gif
+
+This is a path planning code with Informed RRT*.
+
+The cyan ellipse is the heuristic sampling domain of Informed RRT*.
+
+Ref:
+
+-  `Informed RRT\*: Optimal Sampling-based Path Planning Focused via
+   Direct Sampling of an Admissible Ellipsoidal
+   Heuristic <https://arxiv.org/pdf/1404.2334.pdf>`__
+
+.. _batch-informed-rrt*:
+
+Batch Informed RRT\*
+~~~~~~~~~~~~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BatchInformedRRTStar/animation.gif
+
+This is a path planning code with Batch Informed RRT*.
+
+Ref:
+
+-  `Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the
+   Heuristically Guided Search of Implicit Random Geometric
+   Graphs <https://arxiv.org/abs/1405.5848>`__
+
+.. _closed-loop-rrt*:
+
+Closed Loop RRT\*
+~~~~~~~~~~~~~~~~~
+
+A vehicle model based path planning with closed loop RRT*.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ClosedLoopRRTStar/animation.gif
+
+In this code, pure-pursuit algorithm is used for steering control,
+
+PID is used for speed control.
+
+Ref:
+
+-  `Motion Planning in Complex Environments using Closed-loop
+   Prediction <http://acl.mit.edu/papers/KuwataGNC08.pdf>`__
+
+-  `Real-time Motion Planning with Applications to Autonomous Urban
+   Driving <http://acl.mit.edu/papers/KuwataTCST09.pdf>`__
+
+-  `[1601.06326] Sampling-based Algorithms for Optimal Motion Planning
+   Using Closed-loop Prediction <https://arxiv.org/abs/1601.06326>`__
+
+.. _lqr-rrt*:
+
+LQR-RRT\*
+~~~~~~~~~
+
+This is a path planning simulation with LQR-RRT*.
+
+A double integrator motion model is used for LQR local planner.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRRRTStar/animation.gif
+
+Ref:
+
+-  `LQR-RRT\*: Optimal Sampling-Based Motion Planning with Automatically
+   Derived Extension
+   Heuristics <http://lis.csail.mit.edu/pubs/perez-icra12.pdf>`__
+
+-  `MahanFathi/LQR-RRTstar: LQR-RRT\* method is used for random motion planning of a simple pendulum in its phase plot <https://github.com/MahanFathi/LQR-RRTstar>`__

docs/modules/path_planning/rrt/rrt_star.rst

@@ -2,7 +2,6 @@ RRT\*
 ~~~~~
 
 .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTstar/animation.gif
-   :alt: gif
 
 This is a path planning code with RRT\*
 
@@ -11,7 +10,7 @@ Black circles are obstacles, green line is a searched tree, red crosses are star
 Simulation
 ^^^^^^^^^^
 
-.. image:: rrt_star_files/rrt_star_1_0.png
+.. image:: rrt/rrt_star/rrt_star_1_0.png
    :width: 600px
 
 

docs/modules/path_planning/state_lattice_planner/state_lattice_planner.rst

@@ -0,0 +1,33 @@
+State Lattice Planning
+----------------------
+
+This script is a path planning code with state lattice planning.
+
+This code uses the model predictive trajectory generator to solve
+boundary problem.
+
+
+Uniform polar sampling
+~~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/UniformPolarSampling.gif
+
+Biased polar sampling
+~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/BiasedPolarSampling.gif
+
+Lane sampling
+~~~~~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/LaneSampling.gif
+
+Ref:
+
+-  `Optimal rough terrain trajectory generation for wheeled mobile
+   robots <http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
+
+-  `State Space Sampling of Feasible Motions for High-Performance Mobile
+   Robot Navigation in Complex
+   Environments <http://www.frc.ri.cmu.edu/~alonzo/pubs/papers/JFR_08_SS_Sampling.pdf>`__
+

docs/modules/path_planning/vrm_planner/vrm_planner.rst

@@ -0,0 +1,17 @@
+Voronoi Road-Map planning
+-------------------------
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/VoronoiRoadMap/animation.gif
+
+This Voronoi road-map planner uses Dijkstra method for graph search.
+
+In the animation, blue points are Voronoi points,
+
+Cyan crosses mean searched points with Dijkstra method,
+
+The red line is the final path of Vornoi Road-Map.
+
+Ref:
+
+-  `Robotic Motion Planning <https://www.cs.cmu.edu/~motionplanning/lecture/Chap5-RoadMap-Methods_howie.pdf>`__
+