Add "Code Link" sections and rename classes for consistency (#1214)

This commit adds "Code Link" sections to documentation across various path planning modules, linking to relevant class and function APIs. Additionally, several class renaming changes were made, such as `Dijkstra` to `DijkstraPlanner` and `eta3_trajectory` to `Eta3SplineTrajectory`, to enhance naming consistency. Minor fixes include file restructuring and image renaming for the RRT module.

PathPlanning/Dijkstra/dijkstra.py

@@ -12,7 +12,7 @@ import math
 show_animation = True
 
 
-class Dijkstra:
+class DijkstraPlanner:
 
     def __init__(self, ox, oy, resolution, robot_radius):
         """
@@ -246,7 +246,7 @@ def main():
         plt.grid(True)
         plt.axis("equal")
 
-    dijkstra = Dijkstra(ox, oy, grid_size, robot_radius)
+    dijkstra = DijkstraPlanner(ox, oy, grid_size, robot_radius)
     rx, ry = dijkstra.planning(sx, sy, gx, gy)
 
     if show_animation:  # pragma: no cover

PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py

@@ -29,7 +29,7 @@ class MaxVelocityNotReached(Exception):
         self.message = f'Actual velocity {actual_vel} does not equal desired max velocity {max_vel}!'
 
 
-class eta3_trajectory(Eta3Path):
+class Eta3SplineTrajectory(Eta3Path):
     """
     eta3_trajectory
 
@@ -300,8 +300,8 @@ def test1(max_vel=0.5):
         trajectory_segments.append(Eta3PathSegment(
             start_pose=start_pose, end_pose=end_pose, eta=eta, kappa=kappa))
 
-        traj = eta3_trajectory(trajectory_segments,
-                               max_vel=max_vel, max_accel=0.5)
+        traj = Eta3SplineTrajectory(trajectory_segments,
+                                    max_vel=max_vel, max_accel=0.5)
 
         # interpolate at several points along the path
         times = np.linspace(0, traj.total_time, 101)
@@ -334,8 +334,8 @@ def test2(max_vel=0.5):
         trajectory_segments.append(Eta3PathSegment(
             start_pose=start_pose, end_pose=end_pose, eta=eta, kappa=kappa))
 
-        traj = eta3_trajectory(trajectory_segments,
-                               max_vel=max_vel, max_accel=0.5)
+        traj = Eta3SplineTrajectory(trajectory_segments,
+                                    max_vel=max_vel, max_accel=0.5)
 
         # interpolate at several points along the path
         times = np.linspace(0, traj.total_time, 101)
@@ -400,8 +400,8 @@ def test3(max_vel=2.0):
         start_pose=start_pose, end_pose=end_pose, eta=eta, kappa=kappa))
 
     # construct the whole path
-    traj = eta3_trajectory(trajectory_segments,
-                           max_vel=max_vel, max_accel=0.5, max_jerk=1)
+    traj = Eta3SplineTrajectory(trajectory_segments,
+                                max_vel=max_vel, max_accel=0.5, max_jerk=1)
 
     # interpolate at several points along the path
     times = np.linspace(0, traj.total_time, 1001)

docs/modules/5_path_planning/bezier_path/bezier_path_main.rst

@@ -13,8 +13,15 @@ You can get different Beizer course:
 
 .. image:: Figure_2.png
 
+Code Link
+~~~~~~~~~~~~~~~
+
+.. autofunction:: PathPlanning.BezierPath.bezier_path.calc_4points_bezier_path
+
+
 Reference
+~~~~~~~~~~~~~~~
 
 -  `Continuous Curvature Path Generation Based on Bezier Curves for
    Autonomous
-   Vehicles <https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=b00b657c3e0e828c589132a14825e7119772003d>`
+   Vehicles <https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=b00b657c3e0e828c589132a14825e7119772003d>`__

docs/modules/5_path_planning/bspline_path/bspline_path_main.rst

@@ -105,8 +105,8 @@ The default spline degree is 3, so curvature changes smoothly.
 
 .. image:: interp_and_curvature.png
 
-API
-++++
+Code link
+++++++++++
 
 .. autofunction:: PathPlanning.BSplinePath.bspline_path.interpolate_b_spline_path
 
@@ -133,8 +133,8 @@ The default spline degree is 3, so curvature changes smoothly.
 
 .. image:: approx_and_curvature.png
 
-API
-++++
+Code Link
+++++++++++
 
 .. autofunction:: PathPlanning.BSplinePath.bspline_path.approximate_b_spline_path
 

docs/modules/5_path_planning/bugplanner/bugplanner_main.rst

@@ -5,4 +5,12 @@ This is a 2D planning with Bug algorithm.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BugPlanner/animation.gif
 
+Code Link
+~~~~~~~~~~~~~~~
+
+.. autofunction:: PathPlanning.BugPlanning.bug.main
+
+Reference
+~~~~~~~~~~~~
+
 - `ECE452 Bug Algorithms <https://web.archive.org/web/20201103052224/https://sites.google.com/site/ece452bugalgorithms/>`_

docs/modules/5_path_planning/catmull_rom_spline/catmull_rom_spline_main.rst

@@ -88,8 +88,8 @@ Catmull-Rom Spline API
 
 This section provides an overview of the functions used for Catmull-Rom spline path planning.
 
-API
-++++
+Code Link
+++++++++++
 
 .. autofunction:: PathPlanning.Catmull_RomSplinePath.catmull_rom_spline_path.catmull_rom_point
 

docs/modules/5_path_planning/clothoid_path/clothoid_path_main.rst

@@ -73,6 +73,11 @@ The final clothoid path can be calculated with the path parameters and Fresnel i
         &y(s)=y_{0}+\int_{0}^{s} \sin \left(\frac{1}{2} \kappa^{\prime} \tau^{2}+\kappa \tau+\vartheta_{0}\right) \mathrm{d} \tau
         \end{aligned}
 
+Code Link
+~~~~~~~~~~~~~
+
+.. autofunction:: PathPlanning.ClothoidPath.clothoid_path_planner.generate_clothoid_path
+
 
 References
 ~~~~~~~~~~

docs/modules/5_path_planning/coverage_path/coverage_path_main.rst

@@ -8,6 +8,11 @@ This is a 2D grid based sweep coverage path planner simulation:
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/GridBasedSweepCPP/animation.gif
 
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.GridBasedSweepCPP.grid_based_sweep_coverage_path_planner.planning
+
 Spiral Spanning Tree
 ~~~~~~~~~~~~~~~~~~~~
 
@@ -17,6 +22,14 @@ This is a 2D grid based spiral spanning tree coverage path planner simulation:
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/SpiralSpanningTreeCPP/animation2.gif
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/SpiralSpanningTreeCPP/animation3.gif
 
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.SpiralSpanningTreeCPP.spiral_spanning_tree_coverage_path_planner.main
+
+Reference
++++++++++++++
+
 - `Spiral-STC: An On-Line Coverage Algorithm of Grid Environments by a Mobile Robot <https://ieeexplore.ieee.org/abstract/document/1013479>`_
 
 
@@ -29,6 +42,14 @@ This is a 2D grid based wavefront coverage path planner simulation:
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/WavefrontCPP/animation2.gif
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/WavefrontCPP/animation3.gif
 
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.WavefrontCPP.wavefront_coverage_path_planner.wavefront
+
+Reference
++++++++++++++
+
 - `Planning paths of complete coverage of an unstructured environment by a mobile robot <https://pinkwink.kr/attachment/cfile3.uf@1354654A4E8945BD13FE77.pdf>`_
 
 

docs/modules/5_path_planning/cubic_spline/cubic_spline_main.rst

@@ -171,8 +171,8 @@ the second derivative by:
 
 These equations can be calculated by differentiating the cubic polynomial.
 
-API
-===
+Code Link
+==========
 
 This is the 1D cubic spline class API:
 
@@ -199,8 +199,8 @@ Curvature of each point can be also calculated analytically by:
 
 :math:`\kappa=\frac{y^{\prime \prime} x^{\prime}-x^{\prime \prime} y^{\prime}}{\left(x^{\prime2}+y^{\prime2}\right)^{\frac{2}{3}}}`
 
-API
-===
+Code Link
+==========
 
 .. autoclass:: PathPlanning.CubicSpline.cubic_spline_planner.CubicSpline2D
 	:members:

docs/modules/5_path_planning/dubins_path/dubins_path_main.rst

@@ -62,7 +62,7 @@ You can generate a path from these information and the maximum curvature informa
 A path type which has minimum course length among 6 types is selected,
 and then a path is constructed based on the selected type and its distances.
 
-API
+Code Link
 ~~~~~~~~~~~~~~~~~~~~
 
 .. autofunction:: PathPlanning.DubinsPath.dubins_path_planner.plan_dubins_path

docs/modules/5_path_planning/dynamic_window_approach/dynamic_window_approach_main.rst

@@ -5,7 +5,17 @@ Dynamic Window Approach
 
 This is a 2D navigation sample code with Dynamic Window Approach.
 
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DynamicWindowApproach/animation.gif
+
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.DynamicWindowApproach.dynamic_window_approach.dwa_control
+
+
+Reference
+~~~~~~~~~~~~
+
 -  `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/5_path_planning/elastic_bands/elastic_bands_main.rst

@@ -5,6 +5,11 @@ This is a path planning with Elastic Bands.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ElasticBands/animation.gif
 
+Code Link
++++++++++++++
+
+.. autoclass:: PathPlanning.ElasticBands.elastic_bands.ElasticBands
+
 
 Core Concept
 ~~~~~~~~~~~~
@@ -69,6 +74,6 @@ Dynamic Path Maintenance
 - Remove redundant nodes if adjacent nodes are too close
 
 References
-~~~~~~~~~~~~~~~~~~~~~~~
++++++++++++++
 
 -  `Elastic Bands: Connecting Path Planning and Control <http://www8.cs.umu.se/research/ifor/dl/Control/elastic%20bands.pdf>`__

docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst

@@ -7,7 +7,14 @@ Eta^3 Spline path planning
 
 This is a path planning with Eta^3 spline.
 
+Code Link
+~~~~~~~~~~~~~~~
+
+.. autoclass:: PathPlanning.Eta3SplineTrajectory.eta3_spline_trajectory.Eta3SplineTrajectory
+
+
 Reference
+~~~~~~~~~~~~~~~
 
 -  `\\eta^3-Splines for the Smooth Path Generation of Wheeled Mobile
    Robots <https://ieeexplore.ieee.org/document/4339545/>`__

docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst

@@ -7,6 +7,12 @@ The cyan line is the target course and black crosses are obstacles.
 
 The red line is predicted path.
 
+Code Link
+~~~~~~~~~~~~~~
+
+.. autofunction:: PathPlanning.FrenetOptimalTrajectory.frenet_optimal_trajectory.main
+
+
 High Speed and Velocity Keeping Scenario
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst

@@ -10,6 +10,12 @@ This is a 2D grid based path planning with Breadth first search algorithm.
 
 In the animation, cyan points are searched nodes.
 
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.BreadthFirstSearch.breadth_first_search.BreadthFirstSearchPlanner
+
+
 Depth First Search
 ~~~~~~~~~~~~~~~~~~~~
 
@@ -19,6 +25,12 @@ This is a 2D grid based path planning with Depth first search algorithm.
 
 In the animation, cyan points are searched nodes.
 
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.DepthFirstSearch.depth_first_search.DepthFirstSearchPlanner
+
+
 .. _dijkstra:
 
 Dijkstra algorithm
@@ -30,6 +42,12 @@ This is a 2D grid based shortest path planning with Dijkstra's algorithm.
 
 In the animation, cyan points are searched nodes.
 
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.Dijkstra.dijkstra.DijkstraPlanner
+
+
 .. _a*-algorithm:
 
 A\* algorithm
@@ -43,6 +61,12 @@ In the animation, cyan points are searched nodes.
 
 Its heuristic is 2D Euclid distance.
 
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.AStar.a_star.AStarPlanner
+
+
 Bidirectional A\* algorithm
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -52,6 +76,12 @@ This is a 2D grid based shortest path planning with bidirectional A star algorit
 
 In the animation, cyan points are searched nodes.
 
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.BidirectionalAStar.bidirectional_a_star.BidirectionalAStarPlanner
+
+
 .. _D*-algorithm:
 
 D\* algorithm
@@ -63,7 +93,14 @@ This is a 2D grid based shortest path planning with D star algorithm.
 
 The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm.
 
+Code Link
++++++++++++++
+
+.. autoclass:: PathPlanning.DStar.dstar.Dstar
+
+
 Reference
+++++++++++++
 
 -  `D* search Wikipedia <https://en.wikipedia.org/wiki/D*>`__
 
@@ -74,7 +111,13 @@ 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
 
+Code Link
++++++++++++++
+
+.. autoclass:: PathPlanning.DStarLite.d_star_lite.DStarLite
+
 Reference
+++++++++++++
 
 - `Improved Fast Replanning for Robot Navigation in Unknown Terrain <http://www.cs.cmu.edu/~maxim/files/dlite_icra02.pdf>`_
 
@@ -88,7 +131,14 @@ This is a 2D grid based path planning with Potential Field algorithm.
 
 In the animation, the blue heat map shows potential value on each grid.
 
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.PotentialFieldPlanning.potential_field_planning.potential_field_planning
+
+
 Reference
+++++++++++++
 
 -  `Robotic Motion Planning:Potential
    Functions <https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf>`__

docs/modules/5_path_planning/hybridastar/hybridastar_main.rst

@@ -4,3 +4,8 @@ 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
+
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.HybridAStar.hybrid_a_star.hybrid_a_star_planning

docs/modules/5_path_planning/lqr_path/lqr_path_main.rst

@@ -4,3 +4,8 @@ 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
+
+Code Link
++++++++++++++
+
+.. autoclass:: PathPlanning.LQRPlanner.lqr_planner.LQRPlanner

docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst

@@ -6,6 +6,12 @@ generator.
 
 This algorithm is used for state lattice planner.
 
+Code Link
+~~~~~~~~~~~~~
+
+.. autofunction:: PathPlanning.ModelPredictiveTrajectoryGenerator.trajectory_generator.optimize_trajectory
+
+
 Path optimization sample
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -17,6 +23,7 @@ Lookup table generation sample
 .. image:: lookup_table.png
 
 Reference
+~~~~~~~~~~~~
 
 -  `Optimal rough terrain trajectory generation for wheeled mobile
    robots <https://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__

docs/modules/5_path_planning/prm_planner/prm_planner_main.rst

@@ -13,7 +13,14 @@ Cyan crosses means searched points with Dijkstra method,
 
 The red line is the final path of PRM.
 
+Code Link
+~~~~~~~~~~~~~~~
+
+.. autofunction:: PathPlanning.ProbabilisticRoadMap.probabilistic_road_map.prm_planning
+
+
 Reference
+~~~~~~~~~~~
 
 -  `Probabilistic roadmap -
    Wikipedia <https://en.wikipedia.org/wiki/Probabilistic_roadmap>`__

docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst

@@ -9,6 +9,11 @@ Motion planning with quintic polynomials.
 It can calculate 2D path, velocity, and acceleration profile based on
 quintic polynomials.
 
+Code Link
+~~~~~~~~~~~~~~~
+
+.. autofunction:: PathPlanning.QuinticPolynomialsPlanner.quintic_polynomials_planner.quintic_polynomials_planner
+
 
 
 Quintic polynomials for one dimensional robot motion

docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst

@@ -5,6 +5,12 @@ A sample code with Reeds Shepp path planning.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ReedsSheppPath/animation.gif?raw=true
 
+Code Link
+==============
+
+.. autofunction:: PathPlanning.ReedsSheppPath.reeds_shepp_path_planning.reeds_shepp_path_planning
+
+
 Mathematical Description of Individual Path Types
 =================================================
 Here is an overview of mathematical derivations of formulae for individual path types.
@@ -381,6 +387,7 @@ Hence, we have:
 
 
 Reference
+=============
 
 -  `15.3.2 Reeds-Shepp
    Curves <https://lavalle.pl/planning/node822.html>`__

docs/modules/5_path_planning/rrt/rrt_main.rst

@@ -14,6 +14,12 @@ This is a simple path planning code with Rapidly-Exploring Random Trees
 Black circles are obstacles, green line is a searched tree, red crosses
 are start and goal positions.
 
+Code Link
+^^^^^^^^^^
+
+.. autoclass:: PathPlanning.RRT.rrt.RRT
+
+
 .. include:: rrt_star.rst
 
 
@@ -24,6 +30,12 @@ RRT with dubins path
 
 Path planning for a car robot with RRT and dubins path planner.
 
+Code Link
+^^^^^^^^^^
+
+.. autoclass:: PathPlanning.RRTDubins.rrt_dubins.RRTDubins
+
+
 .. _rrt*-with-dubins-path:
 
 RRT\* with dubins path
@@ -33,6 +45,12 @@ RRT\* with dubins path
 
 Path planning for a car robot with RRT\* and dubins path planner.
 
+Code Link
+^^^^^^^^^^
+
+.. autoclass:: PathPlanning.RRTStarDubins.rrt_star_dubins.RRTStarDubins
+
+
 .. _rrt*-with-reeds-sheep-path:
 
 RRT\* with reeds-sheep path
@@ -42,6 +60,12 @@ RRT\* with reeds-sheep path
 
 Path planning for a car robot with RRT\* and reeds sheep path planner.
 
+Code Link
+^^^^^^^^^^
+
+.. autoclass:: PathPlanning.RRTStarReedsShepp.rrt_star_reeds_shepp.RRTStarReedsShepp
+
+
 .. _informed-rrt*:
 
 Informed RRT\*
@@ -53,7 +77,14 @@ This is a path planning code with Informed RRT*.
 
 The cyan ellipse is the heuristic sampling domain of Informed RRT*.
 
+Code Link
+^^^^^^^^^^
+
+.. autoclass:: PathPlanning.InformedRRTStar.informed_rrt_star.InformedRRTStar
+
+
 Reference
+^^^^^^^^^^
 
 -  `Informed RRT\*: Optimal Sampling-based Path Planning Focused via
    Direct Sampling of an Admissible Ellipsoidal
@@ -68,12 +99,20 @@ Batch Informed RRT\*
 
 This is a path planning code with Batch Informed RRT*.
 
+Code Link
+^^^^^^^^^^
+
+.. autoclass:: PathPlanning.BatchInformedRRTStar.batch_informed_rrt_star.BITStar
+
+
 Reference
+^^^^^^^^^^^
 
 -  `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\*
@@ -87,7 +126,14 @@ In this code, pure-pursuit algorithm is used for steering control,
 
 PID is used for speed control.
 
+Code Link
+^^^^^^^^^^
+
+.. autoclass:: PathPlanning.ClosedLoopRRTStar.closed_loop_rrt_star_car.ClosedLoopRRTStar
+
+
 Reference
+^^^^^^^^^^^^
 
 -  `Motion Planning in Complex Environments using Closed-loop
    Prediction <https://acl.mit.edu/papers/KuwataGNC08.pdf>`__
@@ -98,6 +144,7 @@ Reference
 -  `[1601.06326] Sampling-based Algorithms for Optimal Motion Planning
    Using Closed-loop Prediction <https://arxiv.org/abs/1601.06326>`__
 
+
 .. _lqr-rrt*:
 
 LQR-RRT\*
@@ -109,7 +156,14 @@ A double integrator motion model is used for LQR local planner.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRRRTStar/animation.gif
 
+Code Link
+^^^^^^^^^^
+
+.. autoclass:: PathPlanning.LQRRRTStar.lqr_rrt_star.LQRRRTStar
+
+
 Reference
+~~~~~~~~~~~~~
 
 -  `LQR-RRT\*: Optimal Sampling-Based Motion Planning with Automatically
    Derived Extension

docs/modules/5_path_planning/rrt/rrt_star.rst

@@ -7,6 +7,12 @@ This is a path planning code with RRT\*
 
 Black circles are obstacles, green line is a searched tree, red crosses are start and goal positions.
 
+Code Link
+^^^^^^^^^^
+
+.. autoclass:: PathPlanning.RRTStar.rrt_star.RRTStar
+
+
 Simulation
 ^^^^^^^^^^
 

docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst

@@ -12,17 +12,34 @@ Uniform polar sampling
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/UniformPolarSampling.gif
 
+Code Link
+^^^^^^^^^^^^^
+
+.. autofunction:: PathPlanning.StateLatticePlanner.state_lattice_planner.calc_uniform_polar_states
+
+
 Biased polar sampling
 ~~~~~~~~~~~~~~~~~~~~~
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/BiasedPolarSampling.gif
 
+Code Link
+^^^^^^^^^^^^^
+.. autofunction:: PathPlanning.StateLatticePlanner.state_lattice_planner.calc_biased_polar_states
+
+
 Lane sampling
 ~~~~~~~~~~~~~
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/LaneSampling.gif
 
+Code Link
+^^^^^^^^^^^^^
+.. autofunction:: PathPlanning.StateLatticePlanner.state_lattice_planner.calc_lane_states
+
+
 Reference
+~~~~~~~~~~~~~~~
 
 -  `Optimal rough terrain trajectory generation for wheeled mobile
    robots <https://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__

docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst

@@ -33,6 +33,10 @@ After::
 
 When starting at (1, 11) in the structured obstacle arrangement (second of the two gifs above).
 
+Code Link
+^^^^^^^^^^^^^
+.. autoclass:: PathPlanning.TimeBasedPathPlanning.SpaceTimeAStar.SpaceTimeAStar
+
 
 Safe Interval Path Planning
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -73,6 +77,11 @@ Arrangement 1 starting at (1, 18)::
 
 .. image:: https://raw.githubusercontent.com/AtsushiSakai/PythonRoboticsGifs/refs/heads/master/PathPlanning/TimeBasedPathPlanning/SafeIntervalPathPlanner/path_animation2.gif
 
+Code Link
+^^^^^^^^^^^^^
+.. autoclass:: PathPlanning.TimeBasedPathPlanning.SafeInterval.SafeIntervalPathPlanner
+
+
 References
 ~~~~~~~~~~~
 

docs/modules/5_path_planning/visibility_road_map_planner/visibility_road_map_planner_main.rst

@@ -13,6 +13,11 @@ red crosses are visibility nodes, and blue lines area collision free visibility
 
 The red line is the final path searched by dijkstra algorithm frm the visibility graphs.
 
+Code Link
+~~~~~~~~~~~~
+.. autoclass:: PathPlanning.VisibilityRoadMap.visibility_road_map.VisibilityRoadMap
+
+
 Algorithms
 ~~~~~~~~~~
 
@@ -64,7 +69,7 @@ The red line is searched path in the figure:
 You can find the details of Dijkstra algorithm in :ref:`dijkstra`.
 
 References
-^^^^^^^^^^
+~~~~~~~~~~~~
 
 - `Visibility graph - Wikipedia <https://en.wikipedia.org/wiki/Visibility_graph>`_
 

docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst

@@ -11,7 +11,13 @@ Cyan crosses mean searched points with Dijkstra method,
 
 The red line is the final path of Vornoi Road-Map.
 
+Code Link
+~~~~~~~~~~~~~~~
+.. autoclass:: PathPlanning.VoronoiRoadMap.voronoi_road_map.VoronoiRoadMapPlanner
+
+
 Reference
+~~~~~~~~~~~~
 
 -  `Robotic Motion Planning <https://www.cs.cmu.edu/~motionplanning/lecture/Chap5-RoadMap-Methods_howie.pdf>`__
 

tests/test_batch_informed_rrt_star.py

@@ -1,7 +1,7 @@
 import random
 
 import conftest
-from PathPlanning.BatchInformedRRTStar import batch_informed_rrtstar as m
+from PathPlanning.BatchInformedRRTStar import batch_informed_rrt_star as m
 
 
 def test_1():