AtsushiSakai · AtsushiSakai · May 2, 2025 · May 2, 2025
diff --git a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
@@ -5,7 +5,7 @@
 author: Sven Niederberger
         Atsushi Sakai
 
-Ref:
+Reference:
 - Python implementation of 'Successive Convexification for 6-DoF Mars Rocket Powered Landing with Free-Final-Time' paper
 by Michael Szmuk and Behcet Acıkmese.
 

diff --git a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
@@ -5,7 +5,7 @@
 Author: Daniel Ingram (daniel-s-ingram)
         Atsushi Sakai (@Atsushi_twi)
 
-Ref: P. I. Corke, "Robotics, Vision & Control", Springer 2017,
+Reference: P. I. Corke, "Robotics, Vision & Control", Springer 2017,
  ISBN 978-3-319-54413-7 p102
 - [Robotics, Vision and Control]
 (https://link.springer.com/book/10.1007/978-3-642-20144-8)

diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
@@ -4,7 +4,7 @@
 
 author: Ryohei Sasaki(rsasaki0109)
 
-Ref:
+Reference:
 Ensemble Kalman filtering
 (https://rmets.onlinelibrary.wiley.com/doi/10.1256/qj.05.135)
 

diff --git a/Mapping/DistanceMap/distance_map.py b/Mapping/DistanceMap/distance_map.py
@@ -3,7 +3,7 @@
 
 author: Wang Zheng (@Aglargil)
 
-Ref:
+Reference:
 
 - [Distance Map]
 (https://cs.brown.edu/people/pfelzens/papers/dt-final.pdf)

diff --git a/Mapping/rectangle_fitting/rectangle_fitting.py b/Mapping/rectangle_fitting/rectangle_fitting.py
@@ -4,7 +4,7 @@
 
 author: Atsushi Sakai (@Atsushi_twi)
 
-Ref:
+Reference:
 - Efficient L-Shape Fitting for Vehicle Detection Using Laser Scanners -
 The Robotics Institute Carnegie Mellon University
 https://www.ri.cmu.edu/publications/

diff --git a/MissionPlanning/BehaviorTree/behavior_tree.py b/MissionPlanning/BehaviorTree/behavior_tree.py
@@ -3,7 +3,7 @@
 
 author: Wang Zheng (@Aglargil)
 
-Ref:
+Reference:
 
 - [Behavior Tree](https://en.wikipedia.org/wiki/Behavior_tree_(artificial_intelligence,_robotics_and_control))
 """

diff --git a/MissionPlanning/StateMachine/state_machine.py b/MissionPlanning/StateMachine/state_machine.py
@@ -3,7 +3,7 @@
 
 author: Wang Zheng (@Aglargil)
 
-Ref:
+Reference:
 
 - [State Machine]
 (https://en.wikipedia.org/wiki/Finite-state_machine)
@@ -23,7 +23,7 @@ def deflate_and_encode(plantuml_text):
     """
     zlib compress the plantuml text and encode it for the plantuml server.
 
-    Ref: https://plantuml.com/en/text-encoding
+    Reference https://plantuml.com/en/text-encoding
     """
     plantuml_alphabet = (
         string.digits + string.ascii_uppercase + string.ascii_lowercase + "-_"

diff --git a/PathPlanning/ElasticBands/elastic_bands.py b/PathPlanning/ElasticBands/elastic_bands.py
@@ -3,7 +3,7 @@
 
 author: Wang Zheng (@Aglargil)
 
-Ref:
+Reference:
 
 - [Elastic Bands: Connecting Path Planning and Control]
 (http://www8.cs.umu.se/research/ifor/dl/Control/elastic%20bands.pdf)

diff --git a/PathPlanning/Eta3SplinePath/eta3_spline_path.py b/PathPlanning/Eta3SplinePath/eta3_spline_path.py
@@ -5,7 +5,7 @@
 author: Joe Dinius, Ph.D (https://jwdinius.github.io)
         Atsushi Sakai (@Atsushi_twi)
 
-Ref:
+Reference:
 - [eta^3-Splines for the Smooth Path Generation of Wheeled Mobile Robots]
 (https://ieeexplore.ieee.org/document/4339545/)
 

diff --git a/PathPlanning/FrenetOptimalTrajectory/cartesian_frenet_converter.py b/PathPlanning/FrenetOptimalTrajectory/cartesian_frenet_converter.py
@@ -4,7 +4,7 @@
 
 author: Wang Zheng (@Aglargil)
 
-Ref:
+Reference:
 
 - [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)

diff --git a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
@@ -4,7 +4,7 @@
 
 author: Atsushi Sakai (@Atsushi_twi)
 
-Ref:
+Reference:
 
 - [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)

diff --git a/PathPlanning/PotentialFieldPlanning/potential_field_planning.py b/PathPlanning/PotentialFieldPlanning/potential_field_planning.py
@@ -4,7 +4,7 @@
 
 author: Atsushi Sakai (@Atsushi_twi)
 
-Ref:
+Reference:
 https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf
 
 """

diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
@@ -4,7 +4,7 @@
 
 author: Atsushi Sakai (@Atsushi_twi)
 
-Ref:
+Reference:
 
 - [Local Path planning And Motion Control For Agv In Positioning](https://ieeexplore.ieee.org/document/637936/)
 

diff --git a/PathPlanning/StateLatticePlanner/state_lattice_planner.py b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
@@ -8,7 +8,7 @@
 https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathPlanning
 /ModelPredictiveTrajectoryGenerator/lookup_table_generator.py
 
-Ref:
+Reference:
 
 - State Space Sampling of Feasible Motions for High-Performance Mobile Robot
 Navigation in Complex Environments

diff --git a/PathTracking/cgmres_nmpc/cgmres_nmpc.py b/PathTracking/cgmres_nmpc/cgmres_nmpc.py
@@ -4,7 +4,7 @@
 
 author Atsushi Sakai (@Atsushi_twi)
 
-Ref:
+Reference:
 Shunichi09/nonlinear_control: Implementing the nonlinear model predictive
 control, sliding mode control https://github.com/Shunichi09/PythonLinearNonlinearControl
 

diff --git a/PathTracking/move_to_pose/move_to_pose.py b/PathTracking/move_to_pose/move_to_pose.py
@@ -76,7 +76,7 @@ def calc_control_command(self, x_diff, y_diff, theta, theta_goal):
         # [-pi, pi] to prevent unstable behavior e.g. difference going
         # from 0 rad to 2*pi rad with slight turn
 
-        # Ref: The velocity v always has a constant sign which depends on the initial value of α.
+        # The velocity v always has a constant sign which depends on the initial value of α.
         rho = np.hypot(x_diff, y_diff)
         v = self.Kp_rho * rho
 

diff --git a/PathTracking/stanley_controller/stanley_controller.py b/PathTracking/stanley_controller/stanley_controller.py
@@ -4,7 +4,7 @@
 
 author: Atsushi Sakai (@Atsushi_twi)
 
-Ref:
+Reference:
     - [Stanley: The robot that won the DARPA grand challenge](http://isl.ecst.csuchico.edu/DOCS/darpa2005/DARPA%202005%20Stanley.pdf)
     - [Autonomous Automobile Path Tracking](https://www.ri.cmu.edu/pub_files/2009/2/Automatic_Steering_Methods_for_Autonomous_Automobile_Path_Tracking.pdf)
 

diff --git a/README.md b/README.md
@@ -168,7 +168,7 @@ All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation
 
 <img src="https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/extended_kalman_filter/animation.gif" width="640" alt="EKF pic">
 
-Ref:
+Reference
 
 - [documentation](https://atsushisakai.github.io/PythonRobotics/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.html)
 
@@ -186,7 +186,7 @@ It is assumed that the robot can measure a distance from landmarks (RFID).
 
 These measurements are used for PF localization.
 
-Ref:
+Reference
 
 - [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/)
 
@@ -207,7 +207,7 @@ The filter integrates speed input and range observations from RFID for localizat
 
 Initial position is not needed.
 
-Ref:
+Reference
 
 - [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/)
 
@@ -256,7 +256,7 @@ It can calculate a rotation matrix, and a translation vector between points and
 
 ![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/iterative_closest_point/animation.gif)
 
-Ref:
+Reference
 
 - [Introduction to Mobile Robotics: Iterative Closest Point Algorithm](https://cs.gmu.edu/~kosecka/cs685/cs685-icp.pdf)
 
@@ -275,7 +275,7 @@ Black points are landmarks, blue crosses are estimated landmark positions by Fas
 ![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM1/animation.gif)
 
 
-Ref:
+Reference
 
 - [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/)
 
@@ -321,7 +321,7 @@ This is a 2D grid based the shortest path planning with D star algorithm.
 
 The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm.
 
-Ref:
+Reference
 
 - [D* Algorithm Wikipedia](https://en.wikipedia.org/wiki/D*)
 
@@ -346,7 +346,7 @@ 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.
 
-Ref:
+Reference
 
 - [Robotic Motion Planning:Potential Functions](https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf)
 
@@ -362,7 +362,7 @@ This script is a path planning code with state lattice planning.
 
 This code uses the model predictive trajectory generator to solve boundary problem.
 
-Ref: 
+Reference 
 
 - [Optimal rough terrain trajectory generation for wheeled mobile robots](https://journals.sagepub.com/doi/pdf/10.1177/0278364906075328)
 
@@ -390,7 +390,7 @@ Cyan crosses means searched points with Dijkstra method,
 
 The red line is the final path of PRM.
 
-Ref:
+Reference
 
 - [Probabilistic roadmap \- Wikipedia](https://en.wikipedia.org/wiki/Probabilistic_roadmap)
 
@@ -406,7 +406,7 @@ 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.
 
-Ref:
+Reference
 
 - [Incremental Sampling-based Algorithms for Optimal Motion Planning](https://arxiv.org/abs/1005.0416)
 
@@ -426,7 +426,7 @@ A double integrator motion model is used for LQR local planner.
 
 ![LQR_RRT](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRRRTStar/animation.gif)
 
-Ref:
+Reference
 
 - [LQR\-RRT\*: Optimal Sampling\-Based Motion Planning with Automatically Derived Extension Heuristics](https://lis.csail.mit.edu/pubs/perez-icra12.pdf)
 
@@ -441,7 +441,7 @@ Motion planning with quintic polynomials.
 
 It can calculate a 2D path, velocity, and acceleration profile based on quintic polynomials.
 
-Ref:
+Reference
 
 - [Local Path Planning And Motion Control For Agv In Positioning](https://ieeexplore.ieee.org/document/637936/)
 
@@ -451,7 +451,7 @@ A sample code with Reeds Shepp path planning.
 
 ![RSPlanning](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ReedsSheppPath/animation.gif?raw=true)
 
-Ref:
+Reference
 
 - [15.3.2 Reeds\-Shepp Curves](http://planning.cs.uiuc.edu/node822.html) 
 
@@ -477,7 +477,7 @@ The cyan line is the target course and black crosses are obstacles.
 
 The red line is the predicted path.
 
-Ref:
+Reference
 
 - [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)
 
@@ -492,7 +492,7 @@ This is a simulation of moving to a pose control
 
 ![2](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif)
 
-Ref:
+Reference
 
 - [P. I. Corke, "Robotics, Vision and Control" \| SpringerLink p102](https://link.springer.com/book/10.1007/978-3-642-20144-8)
 
@@ -503,7 +503,7 @@ Path tracking simulation with Stanley steering control and PID speed control.
 
 ![2](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/stanley_controller/animation.gif)
 
-Ref:
+Reference
 
 - [Stanley: The robot that won the DARPA grand challenge](http://robots.stanford.edu/papers/thrun.stanley05.pdf)
 
@@ -517,7 +517,7 @@ Path tracking simulation with rear wheel feedback steering control and PID speed
 
 ![PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/rear_wheel_feedback/animation.gif)
 
-Ref:
+Reference
 
 - [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://arxiv.org/abs/1604.07446)
 
@@ -528,7 +528,7 @@ Path tracking simulation with LQR speed and steering control.
 
 ![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_speed_steer_control/animation.gif)
 
-Ref:
+Reference
 
 - [Towards fully autonomous driving: Systems and algorithms \- IEEE Conference Publication](https://ieeexplore.ieee.org/document/5940562/)
 
@@ -539,7 +539,7 @@ Path tracking simulation with iterative linear model predictive speed and steeri
 
 <img src="https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/model_predictive_speed_and_steer_control/animation.gif" width="640" alt="MPC pic">
 
-Ref:
+Reference
 
 - [documentation](https://atsushisakai.github.io/PythonRobotics/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.html)
 
@@ -551,7 +551,7 @@ A motion planning and path tracking simulation with NMPC of C-GMRES
 
 ![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/cgmres_nmpc/animation.gif)
 
-Ref:
+Reference
 
 - [documentation](https://atsushisakai.github.io/PythonRobotics/modules/path_tracking/cgmres_nmpc/cgmres_nmpc.html)
 
@@ -591,7 +591,7 @@ This is a 3d trajectory generation simulation for a rocket powered landing.
 
 ![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/AerialNavigation/rocket_powered_landing/animation.gif)
 
-Ref:
+Reference
 
 - [documentation](https://atsushisakai.github.io/PythonRobotics/modules/aerial_navigation/rocket_powered_landing/rocket_powered_landing.html)
 

diff --git a/docs/modules/12_appendix/Kalmanfilter_basics_2_main.rst b/docs/modules/12_appendix/Kalmanfilter_basics_2_main.rst
@@ -331,7 +331,7 @@ are vectors and matrices, but the concepts are exactly the same:
 -  Use the process model to predict the next state (the prior)
 -  Form an estimate part way between the measurement and the prior
 
-References:
+Reference
 ~~~~~~~~~~~
 
 1. Roger Labbe’s

diff --git a/docs/modules/12_appendix/Kalmanfilter_basics_main.rst b/docs/modules/12_appendix/Kalmanfilter_basics_main.rst
@@ -552,7 +552,7 @@ a given (X,Y) value.
 .. image:: Kalmanfilter_basics_files/Kalmanfilter_basics_28_1.png
 
 
-References:
+Reference
 ~~~~~~~~~~~
 
 1. Roger Labbe’s

diff --git a/docs/modules/12_appendix/steering_motion_model_main.rst b/docs/modules/12_appendix/steering_motion_model_main.rst
@@ -91,7 +91,7 @@ the target minimum velocity :math:`v_{min}` can be calculated as follows:
 :math:`v_{min} = \frac{d_{t+1}+d_{t}}{\Delta t} = \frac{d_{t+1}+d_{t}}{(\kappa_{t+1}-\kappa_{t})}\frac{tan\dot{\delta}_{max}}{WB}`
 
 
-References:
+Reference
 ~~~~~~~~~~~
 
 - `Vehicle Dynamics and Control <https://link.springer.com/book/10.1007/978-1-4614-1433-9>`_
diff --git a/...e_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst b/...e_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst
@@ -5,3 +5,8 @@ Ensamble Kalman Filter Localization
 
 This is a sensor fusion localization with Ensamble Kalman Filter(EnKF).
 
+Code Link
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: Localization.ensemble_kalman_filter.ensemble_kalman_filter.enkf_localization
+
-Original file line number
+Diff line change
@@ Expand Up / @@ -3,7 +3,7 @@ @@
     author: Wang Zheng (@Aglargil)
-    Ref:
+    Reference:
     - [Behavior Tree](https://en.wikipedia.org/wiki/Behavior_tree_(artificial_intelligence,_robotics_and_control))
     """
@@ Expand Down @@