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Documentations related codes should be in the python script as the header comments of the script or docstrings of each function. +Also, each document should have a link to the code in Github. +You can easily add the link by using the `.. autoclass::`, `.. autofunction::`, and `.. automodule` by Sphinx's `autodoc`_ module. + +Using this `autodoc`_ module, the generated documentations have the link to the code in Github like: + +.. image:: /_static/img/source_link_1.png + +When you click the link, you will jump to the source code in Github like: + +.. image:: /_static/img/source_link_2.png + + .. _`submit a pull request`: @@ -210,5 +222,6 @@ Current Major Sponsors: .. _`1Password`: https://github.com/1Password/for-open-source .. _`matplotrecorder`: https://github.com/AtsushiSakai/matplotrecorder .. _`PythonRoboticsGifs`: https://github.com/AtsushiSakai/PythonRoboticsGifs +.. _`autodoc`: https://www.sphinx-doc.org/en/master/usage/extensions/autodoc.html From f466f25f5328e019bd1e0ccc62b176ff2fc72187 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 28 Apr 2025 08:53:35 +0900 Subject: [PATCH 02/81] build(deps): bump ruff from 0.11.0 to 0.11.6 in /requirements (#1201) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.11.0 to 0.11.6. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.11.0...0.11.6) --- updated-dependencies: - dependency-name: ruff dependency-version: 0.11.6 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 5438678f87..f3e6c3dc51 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.5 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.15.0 # For unit test -ruff == 0.11.0 # For unit test +ruff == 0.11.6 # For unit test From af0442d358b1deca5710c0c6812424b12b39f7e7 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 1 May 2025 13:08:29 +0900 Subject: [PATCH 03/81] build(deps): update cvxpy version from 1.5.3 to 1.6.5 in requirements (#1203) * build(deps): update cvxpy version from 1.5.3 to 1.6.5 in requirements * Add ECOS solver and improve solver handling for stability Added ECOS to requirements and enhanced compatibility with cvxpy solvers by specifying 'order' for matrix reshaping. Updated solver configurations in rocket landing and pendulum control for consistency and reliability. Improved test behavior by enforcing stricter warning handling in pytest. --- .../rocket_powered_landing/rocket_powered_landing.py | 9 +++++---- InvertedPendulum/inverted_pendulum_mpc_control.py | 2 +- requirements/requirements.txt | 3 ++- tests/conftest.py | 2 +- 4 files changed, 9 insertions(+), 7 deletions(-) diff --git a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py index 239f3629c1..1918dc1cee 100644 --- a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py +++ b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py @@ -31,6 +31,7 @@ W_DELTA_SIGMA = 1e-1 # difference in flight time W_NU = 1e5 # virtual control +print(cvxpy.installed_solvers()) solver = 'ECOS' verbose_solver = False @@ -462,11 +463,11 @@ def __init__(self, m, K): # x_t+1 = A_*x_t+B_*U_t+C_*U_T+1*S_*sigma+zbar+nu constraints += [ self.var['X'][:, k + 1] == - cvxpy.reshape(self.par['A_bar'][:, k], (m.n_x, m.n_x)) @ + cvxpy.reshape(self.par['A_bar'][:, k], (m.n_x, m.n_x), order='F') @ self.var['X'][:, k] + - cvxpy.reshape(self.par['B_bar'][:, k], (m.n_x, m.n_u)) @ + cvxpy.reshape(self.par['B_bar'][:, k], (m.n_x, m.n_u), order='F') @ self.var['U'][:, k] + - cvxpy.reshape(self.par['C_bar'][:, k], (m.n_x, m.n_u)) @ + cvxpy.reshape(self.par['C_bar'][:, k], (m.n_x, m.n_u), order='F') @ self.var['U'][:, k + 1] + self.par['S_bar'][:, k] * self.var['sigma'] + self.par['z_bar'][:, k] + @@ -536,7 +537,7 @@ def solve(self, **kwargs): with warnings.catch_warnings(): # For User warning from solver warnings.simplefilter('ignore') self.prob.solve(verbose=verbose_solver, - solver=solver) + solver=solver) except cvxpy.SolverError: error = True diff --git a/InvertedPendulum/inverted_pendulum_mpc_control.py b/InvertedPendulum/inverted_pendulum_mpc_control.py index 9a5fa2ab41..c45dde8acc 100644 --- a/InvertedPendulum/inverted_pendulum_mpc_control.py +++ b/InvertedPendulum/inverted_pendulum_mpc_control.py @@ -91,7 +91,7 @@ def mpc_control(x0): prob = cvxpy.Problem(cvxpy.Minimize(cost), constr) start = time.time() - prob.solve(verbose=False) + prob.solve(verbose=False, solver=cvxpy.CLARABEL) elapsed_time = time.time() - start print(f"calc time:{elapsed_time:.6f} [sec]") diff --git a/requirements/requirements.txt b/requirements/requirements.txt index f3e6c3dc51..e9c68be7e6 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,7 +1,8 @@ numpy == 2.2.4 scipy == 1.15.2 matplotlib == 3.10.1 -cvxpy == 1.5.3 +cvxpy == 1.6.5 +ecos == 2.0.14 pytest == 8.3.5 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.15.0 # For unit test diff --git a/tests/conftest.py b/tests/conftest.py index 3485fe8150..b707b22d00 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -10,4 +10,4 @@ def run_this_test(file): - pytest.main([os.path.abspath(file)]) + pytest.main(args=["-W", "error", "-Werror", "--pythonwarnings=error", os.path.abspath(file)]) From 1f729cb8cbd5075871bb52c8fb31df2732ad6641 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 1 May 2025 13:59:15 +0900 Subject: [PATCH 04/81] build(deps): bump ruff from 0.11.6 to 0.11.7 in /requirements (#1205) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.11.6 to 0.11.7. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.11.6...0.11.7) --- updated-dependencies: - dependency-name: ruff dependency-version: 0.11.7 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index e9c68be7e6..88cbb21f81 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -6,4 +6,4 @@ ecos == 2.0.14 pytest == 8.3.5 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.15.0 # For unit test -ruff == 0.11.6 # For unit test +ruff == 0.11.7 # For unit test From a2c42c3d6837658edb734e9f16ab64c1bd2662e7 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 1 May 2025 21:53:12 +0900 Subject: [PATCH 05/81] Update Python version to 3.13 across the project (#1208) * Update Python version to 3.13 across the project Upgraded the required Python version to 3.13 in configurations, CI workflows, documentation, and environment files. This ensures compatibility with the latest Python release and maintains consistency across all project components. * Update Python version to 3.13 across the project Upgraded the required Python version to 3.13 in configurations, CI workflows, documentation, and environment files. This ensures compatibility with the latest Python release and maintains consistency across all project components. --- .circleci/config.yml | 2 +- .github/workflows/Linux_CI.yml | 2 +- .github/workflows/MacOS_CI.yml | 2 +- .github/workflows/Windows_CI.yml | 2 +- README.md | 2 +- appveyor.yml | 2 +- docs/modules/0_getting_started/3_how_to_contribute_main.rst | 2 +- requirements/environment.yml | 2 +- ruff.toml | 4 ++-- 9 files changed, 10 insertions(+), 10 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 9f803ece4a..f6eff674de 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -6,7 +6,7 @@ orbs: jobs: build_doc: docker: - - image: cimg/python:3.12 + - image: cimg/python:3.13 steps: - checkout - run: diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml index 7b3dc14751..907d36452d 100644 --- a/.github/workflows/Linux_CI.yml +++ b/.github/workflows/Linux_CI.yml @@ -12,7 +12,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [ '3.12' ] + python-version: [ '3.13' ] name: Python ${{ matrix.python-version }} CI diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml index 5ea15ac72e..03db65f43d 100644 --- a/.github/workflows/MacOS_CI.yml +++ b/.github/workflows/MacOS_CI.yml @@ -16,7 +16,7 @@ jobs: runs-on: macos-latest strategy: matrix: - python-version: [ '3.12' ] + python-version: [ '3.13' ] name: Python ${{ matrix.python-version }} CI steps: - uses: actions/checkout@v4 diff --git a/.github/workflows/Windows_CI.yml b/.github/workflows/Windows_CI.yml index b9c8dea649..5cb19699b2 100644 --- a/.github/workflows/Windows_CI.yml +++ b/.github/workflows/Windows_CI.yml @@ -16,7 +16,7 @@ jobs: runs-on: windows-latest strategy: matrix: - python-version: [ '3.12' ] + python-version: [ '3.13' ] name: Python ${{ matrix.python-version }} CI steps: - uses: actions/checkout@v4 diff --git a/README.md b/README.md index 9e605435ce..6d04c90bc8 100644 --- a/README.md +++ b/README.md @@ -102,7 +102,7 @@ or this paper for more details: For running each sample code: -- [Python 3.12.x](https://www.python.org/) +- [Python 3.13.x](https://www.python.org/) - [NumPy](https://numpy.org/) diff --git a/appveyor.yml b/appveyor.yml index 05ad8a2820..72d89acf11 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -8,7 +8,7 @@ environment: CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\appveyor\\run_with_env.cmd" matrix: - - PYTHON_DIR: C:\Python310-x64 + - PYTHON_DIR: C:\Python313-x64 branches: only: diff --git a/docs/modules/0_getting_started/3_how_to_contribute_main.rst b/docs/modules/0_getting_started/3_how_to_contribute_main.rst index 30e3001f36..0325aaacae 100644 --- a/docs/modules/0_getting_started/3_how_to_contribute_main.rst +++ b/docs/modules/0_getting_started/3_how_to_contribute_main.rst @@ -26,7 +26,7 @@ to understand the philosophies of this project. Check your Python version. --------------------------- -We only accept a PR for Python 3.12.x or higher. +We only accept a PR for Python 3.13.x or higher. We will not accept a PR for Python 2.x. diff --git a/requirements/environment.yml b/requirements/environment.yml index afbb3fb8ce..023a3d75bf 100644 --- a/requirements/environment.yml +++ b/requirements/environment.yml @@ -2,7 +2,7 @@ name: python_robotics channels: - conda-forge dependencies: - - python=3.12 + - python=3.13 - pip - scipy - numpy diff --git a/ruff.toml b/ruff.toml index 5823ca3db7..d76b715a06 100644 --- a/ruff.toml +++ b/ruff.toml @@ -5,8 +5,8 @@ ignore = ["E501", "E741", "E402"] exclude = [ ] -# Assume Python 3.11 -target-version = "py312" +# Assume Python 3.13 +target-version = "py313" [per-file-ignores] From 22cbee49218a1f8acb07ad488416db3c952c19d6 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Fri, 2 May 2025 10:01:19 +0900 Subject: [PATCH 06/81] Standardize "Ref:" to "Reference" across files (#1210) Updated all instances of "Ref:" to "Reference" for consistency in both code and documentation. This change improves clarity and aligns with standard terminology practices. --- .../rocket_powered_landing.py | 2 +- .../two_joint_arm_to_point_control.py | 2 +- .../ensemble_kalman_filter.py | 2 +- Mapping/DistanceMap/distance_map.py | 2 +- .../rectangle_fitting/rectangle_fitting.py | 2 +- MissionPlanning/BehaviorTree/behavior_tree.py | 2 +- MissionPlanning/StateMachine/state_machine.py | 4 +- PathPlanning/ElasticBands/elastic_bands.py | 2 +- .../Eta3SplinePath/eta3_spline_path.py | 2 +- .../cartesian_frenet_converter.py | 2 +- .../frenet_optimal_trajectory.py | 2 +- .../potential_field_planning.py | 2 +- .../quintic_polynomials_planner.py | 2 +- .../state_lattice_planner.py | 2 +- PathTracking/cgmres_nmpc/cgmres_nmpc.py | 2 +- PathTracking/move_to_pose/move_to_pose.py | 2 +- .../stanley_controller/stanley_controller.py | 2 +- README.md | 42 ++--- .../Kalmanfilter_basics_2_main.rst | 2 +- .../12_appendix/Kalmanfilter_basics_main.rst | 2 +- .../steering_motion_model_main.rst | 2 +- ...samble_kalman_filter_localization_main.rst | 5 + ...tended_kalman_filter_localization_main.rst | 168 +++++++++--------- .../histogram_filter_localization_main.rst | 7 +- .../particle_filter_localization_main.rst | 8 +- ...cented_kalman_filter_localization_main.rst | 8 +- .../modules/4_slam/ekf_slam/ekf_slam_main.rst | 2 +- .../4_slam/graph_slam/graph_slam_main.rst | 2 +- .../bezier_path/bezier_path_main.rst | 2 +- .../eta3_spline/eta3_spline_main.rst | 2 +- .../frenet_frame_path_main.rst | 2 +- .../grid_base_search_main.rst | 6 +- ...l_predictive_trajectory_generator_main.rst | 2 +- .../prm_planner/prm_planner_main.rst | 2 +- .../quintic_polynomials_planner_main.rst | 2 +- .../reeds_shepp_path_main.rst | 2 +- docs/modules/5_path_planning/rrt/rrt_main.rst | 8 +- .../state_lattice_planner_main.rst | 2 +- .../vrm_planner/vrm_planner_main.rst | 2 +- .../lqr_speed_and_steering_control_main.rst | 2 +- .../lqr_steering_control_main.rst | 2 +- .../pure_pursuit_tracking_main.rst | 2 +- .../rear_wheel_feedback_control_main.rst | 2 +- .../stanley_control/stanley_control_main.rst | 2 +- users_comments.md | 2 +- 45 files changed, 173 insertions(+), 155 deletions(-) diff --git a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py index 1918dc1cee..e8ba8fa220 100644 --- 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 index c2227f18e3..09969c30be 100644 --- 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 index 2bab3b49c1..e8a988a270 100644 --- 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 index 0dcc7380c5..0f96c9e8c6 100644 --- 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 index 177f078871..7902619666 100644 --- 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 index 59f4c713f1..9ad886aafb 100644 --- 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 index de72f0f451..454759236e 100644 --- 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 index 785f822d14..77d4e6e399 100644 --- 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 index dc07d3c84b..3f685e512f 100644 --- 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 index 4cc8650c89..482712ceaf 100644 --- 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 index 248894c1c6..4b82fb70fd 100644 --- 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 index 8f136b5ee3..603a9d16cf 100644 --- 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 index fdc181afab..86f9f662da 100644 --- 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 index 7f8e725e0a..05f8df78f8 100644 --- 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 index a582c9da81..ee40e73504 100644 --- 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 index 34736a2e21..faf1264953 100644 --- 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 index bc98175f17..01c2ec0229 100644 --- 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 index 6d04c90bc8..05fd0262df 100644 --- a/README.md +++ b/README.md @@ -168,7 +168,7 @@ All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation 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 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 index 9ae6fc5bcb..b7ff84e6f6 100644 --- 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 index 6548377e07..a1d99d47ef 100644 --- 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 index 6e444b7909..c697123fa2 100644 --- 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 `_ diff --git a/docs/modules/2_localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst b/docs/modules/2_localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst index 2543d1186a..214e645d10 100644 --- a/docs/modules/2_localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst +++ b/docs/modules/2_localization/ensamble_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 + diff --git a/docs/modules/2_localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst b/docs/modules/2_localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst index da214b6de5..adb41e5e40 100644 --- a/docs/modules/2_localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst +++ b/docs/modules/2_localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst @@ -23,28 +23,40 @@ is estimated trajectory with EKF. The red ellipse is estimated covariance ellipse with EKF. -Code: `PythonRobotics/extended_kalman_filter.py at master · -AtsushiSakai/PythonRobotics `__ +Code Link +~~~~~~~~~~~~~ -Kalman Filter with Speed Scale Factor Correction -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +.. autofunction:: Localization.extended_kalman_filter.extended_kalman_filter.ekf_estimation +Extended Kalman Filter algorithm +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. image:: ekf_with_velocity_correction_1_0.png - :width: 600px +Localization process using Extended Kalman Filter:EKF is -This is a Extended kalman filter (EKF) localization with velocity correction. +=== Predict === -This is for correcting the vehicle speed measured with scale factor errors due to factors such as wheel wear. +:math:`x_{Pred} = Fx_t+Bu_t` -Code: `PythonRobotics/extended_kalman_ekf_with_velocity_correctionfilter.py -AtsushiSakai/PythonRobotics `__ +:math:`P_{Pred} = J_f P_t J_f^T + Q` -Filter design -~~~~~~~~~~~~~ +=== Update === -Position Estimation Kalman Filter -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +:math:`z_{Pred} = Hx_{Pred}` + +:math:`y = z - z_{Pred}` + +:math:`S = J_g P_{Pred}.J_g^T + R` + +:math:`K = P_{Pred}.J_g^T S^{-1}` + +:math:`x_{t+1} = x_{Pred} + Ky` + +:math:`P_{t+1} = ( I - K J_g) P_{Pred}` + + + +Filter design +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In this simulation, the robot has a state vector includes 4 states at time :math:`t`. @@ -82,27 +94,9 @@ In the code, “observation” function generates the input and observation vector with noise `code `__ -Kalman Filter with Speed Scale Factor Correction -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -In this simulation, the robot has a state vector includes 5 states at -time :math:`t`. - -.. math:: \textbf{x}_t=[x_t, y_t, \phi_t, v_t, s_t] - -x, y are a 2D x-y position, :math:`\phi` is orientation, v is -velocity, and s is a scale factor of velocity. - -In the code, “xEst” means the state vector. -`code `__ - -The rest is the same as the Position Estimation Kalman Filter. Motion Model -~~~~~~~~~~~~ - -Position Estimation Kalman Filter -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +~~~~~~~~~~~~~~~~~ The robot model is @@ -137,9 +131,61 @@ Its Jacobian matrix is :math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & -v \sin(\phi) \Delta t & \cos(\phi) \Delta t\\ 0 & 1 & v \cos(\phi) \Delta t & \sin(\phi) \Delta t\\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 \end{bmatrix} \end{equation*}` +Observation Model +~~~~~~~~~~~~~~~~~ + +The robot can get x-y position information from GPS. + +So GPS Observation model is + +.. math:: \textbf{z}_{t} = g(\textbf{x}_t) = H \textbf{x}_t + +where + +:math:`\begin{equation*} H = \begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ \end{bmatrix} \end{equation*}` + +The observation function states that + +:math:`\begin{equation*} \begin{bmatrix} x' \\ y' \end{bmatrix} = g(\textbf{x}) = \begin{bmatrix} x \\ y \end{bmatrix} \end{equation*}` + +Its Jacobian matrix is + +:math:`\begin{equation*} J_g = \begin{bmatrix} \frac{\partial x'}{\partial x} & \frac{\partial x'}{\partial y} & \frac{\partial x'}{\partial \phi} & \frac{\partial x'}{\partial v}\\ \frac{\partial y'}{\partial x}& \frac{\partial y'}{\partial y} & \frac{\partial y'}{\partial \phi} & \frac{\partial y'}{ \partial v}\\ \end{bmatrix} \end{equation*}` + +:math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & 0 & 0\\ 0 & 1 & 0 & 0\\ \end{bmatrix} \end{equation*}` + Kalman Filter with Speed Scale Factor Correction ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +This is a Extended kalman filter (EKF) localization with velocity correction. + +This is for correcting the vehicle speed measured with scale factor errors due to factors such as wheel wear. + + +In this simulation, the robot has a state vector includes 5 states at +time :math:`t`. + +.. math:: \textbf{x}_t=[x_t, y_t, \phi_t, v_t, s_t] + +x, y are a 2D x-y position, :math:`\phi` is orientation, v is +velocity, and s is a scale factor of velocity. + +In the code, “xEst” means the state vector. +`code `__ + +The rest is the same as the Position Estimation Kalman Filter. + +.. image:: ekf_with_velocity_correction_1_0.png + :width: 600px + +Code Link +~~~~~~~~~~~~~ + +.. autofunction:: Localization.extended_kalman_filter.ekf_with_velocity_correction.ekf_estimation + + +Motion Model +~~~~~~~~~~~~ The robot model is @@ -178,33 +224,7 @@ Its Jacobian matrix is Observation Model ~~~~~~~~~~~~~~~~~ -Position Estimation Kalman Filter -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The robot can get x-y position infomation from GPS. - -So GPS Observation model is - -.. math:: \textbf{z}_{t} = g(\textbf{x}_t) = H \textbf{x}_t - -where - -:math:`\begin{equation*} H = \begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ \end{bmatrix} \end{equation*}` - -The observation function states that - -:math:`\begin{equation*} \begin{bmatrix} x' \\ y' \end{bmatrix} = g(\textbf{x}) = \begin{bmatrix} x \\ y \end{bmatrix} \end{equation*}` - -Its Jacobian matrix is - -:math:`\begin{equation*} J_g = \begin{bmatrix} \frac{\partial x'}{\partial x} & \frac{\partial x'}{\partial y} & \frac{\partial x'}{\partial \phi} & \frac{\partial x'}{\partial v}\\ \frac{\partial y'}{\partial x}& \frac{\partial y'}{\partial y} & \frac{\partial y'}{\partial \phi} & \frac{\partial y'}{ \partial v}\\ \end{bmatrix} \end{equation*}` - -:math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & 0 & 0\\ 0 & 1 & 0 & 0\\ \end{bmatrix} \end{equation*}` - -Kalman Filter with Speed Scale Factor Correction -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The robot can get x-y position infomation from GPS. +The robot can get x-y position information from GPS. So GPS Observation model is @@ -225,32 +245,8 @@ Its Jacobian matrix is :math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & 0 & 0 & 0\\ 0 & 1 & 0 & 0 & 0\\ \end{bmatrix} \end{equation*}` -Extended Kalman Filter -~~~~~~~~~~~~~~~~~~~~~~ - -Localization process using Extended Kalman Filter:EKF is - -=== Predict === - -:math:`x_{Pred} = Fx_t+Bu_t` - -:math:`P_{Pred} = J_f P_t J_f^T + Q` - -=== Update === - -:math:`z_{Pred} = Hx_{Pred}` - -:math:`y = z - z_{Pred}` - -:math:`S = J_g P_{Pred}.J_g^T + R` - -:math:`K = P_{Pred}.J_g^T S^{-1}` - -:math:`x_{t+1} = x_{Pred} + Ky` - -:math:`P_{t+1} = ( I - K J_g) P_{Pred}` -Ref: -~~~~ +Reference +^^^^^^^^^^^ - `PROBABILISTIC-ROBOTICS.ORG `__ diff --git a/docs/modules/2_localization/histogram_filter_localization/histogram_filter_localization_main.rst b/docs/modules/2_localization/histogram_filter_localization/histogram_filter_localization_main.rst index fafd578333..3a175b1316 100644 --- a/docs/modules/2_localization/histogram_filter_localization/histogram_filter_localization_main.rst +++ b/docs/modules/2_localization/histogram_filter_localization/histogram_filter_localization_main.rst @@ -16,6 +16,11 @@ The filter uses speed input and range observations from RFID for localization. Initial position information is not needed. +Code Link +~~~~~~~~~~~~~ + +.. autofunction:: Localization.histogram_filter.histogram_filter.histogram_filter_localization + Filtering algorithm ~~~~~~~~~~~~~~~~~~~~ @@ -107,7 +112,7 @@ There are two ways to calculate the final positions: -References: +Reference ~~~~~~~~~~~ - `_PROBABILISTIC ROBOTICS: `_ diff --git a/docs/modules/2_localization/particle_filter_localization/particle_filter_localization_main.rst b/docs/modules/2_localization/particle_filter_localization/particle_filter_localization_main.rst index 20a9eb58fc..d648d8e080 100644 --- a/docs/modules/2_localization/particle_filter_localization/particle_filter_localization_main.rst +++ b/docs/modules/2_localization/particle_filter_localization/particle_filter_localization_main.rst @@ -15,6 +15,12 @@ It is assumed that the robot can measure a distance from landmarks This measurements are used for PF localization. +Code Link +~~~~~~~~~~~~~ + +.. autofunction:: Localization.particle_filter.particle_filter.pf_localization + + How to calculate covariance matrix from particles ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -30,7 +36,7 @@ The covariance matrix :math:`\Xi` from particle information is calculated by the - :math:`\mu_j` is the :math:`j` th mean state of particles. -References: +Reference ~~~~~~~~~~~ - `_PROBABILISTIC ROBOTICS: `_ diff --git a/docs/modules/2_localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization_main.rst b/docs/modules/2_localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization_main.rst index bb6b5b664b..a7a5aab61b 100644 --- a/docs/modules/2_localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization_main.rst +++ b/docs/modules/2_localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization_main.rst @@ -7,7 +7,13 @@ This is a sensor fusion localization with Unscented Kalman Filter(UKF). The lines and points are same meaning of the EKF simulation. -References: +Code Link +~~~~~~~~~~~~~ + +.. autofunction:: Localization.unscented_kalman_filter.unscented_kalman_filter.ukf_estimation + + +Reference ~~~~~~~~~~~ - `Discriminatively Trained Unscented Kalman Filter for Mobile Robot Localization `_ diff --git a/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst b/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst index b27971225e..a1486ffe1e 100644 --- a/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst +++ b/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst @@ -578,7 +578,7 @@ reckoning and control functions are passed along here as well. .. image:: ekf_slam_1_0.png -References: +Reference ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - `PROBABILISTIC ROBOTICS `_ diff --git a/docs/modules/4_slam/graph_slam/graph_slam_main.rst b/docs/modules/4_slam/graph_slam/graph_slam_main.rst index 987eed385c..a2ef513527 100644 --- a/docs/modules/4_slam/graph_slam/graph_slam_main.rst +++ b/docs/modules/4_slam/graph_slam/graph_slam_main.rst @@ -17,7 +17,7 @@ The black stars are landmarks for graph edge generation. .. include:: graphSLAM_formulation.rst .. include:: graphSLAM_SE2_example.rst -References: +Reference ~~~~~~~~~~~ - `A Tutorial on Graph-Based SLAM `_ diff --git a/docs/modules/5_path_planning/bezier_path/bezier_path_main.rst b/docs/modules/5_path_planning/bezier_path/bezier_path_main.rst index d306a85352..9d9b3de709 100644 --- a/docs/modules/5_path_planning/bezier_path/bezier_path_main.rst +++ b/docs/modules/5_path_planning/bezier_path/bezier_path_main.rst @@ -13,7 +13,7 @@ You can get different Beizer course: .. image:: Figure_2.png -Ref: +Reference - `Continuous Curvature Path Generation Based on Bezier Curves for Autonomous diff --git a/docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst b/docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst index ffc3cc6451..82e0a71044 100644 --- a/docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst +++ b/docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst @@ -7,7 +7,7 @@ Eta^3 Spline path planning This is a path planning with Eta^3 spline. -Ref: +Reference - `\\eta^3-Splines for the Smooth Path Generation of Wheeled Mobile Robots `__ diff --git a/docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst b/docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst index 38efaf2b53..371d536e3f 100644 --- a/docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst +++ b/docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst @@ -35,7 +35,7 @@ Low Speed and Merging and Stopping Scenario This scenario illustrates the trajectory planning at low speeds with merging and stopping behaviors. -Ref: +Reference - `Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet diff --git a/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst b/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst index 1644ed00cc..bf82c9f391 100644 --- a/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst +++ b/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst @@ -63,7 +63,7 @@ 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. -Ref: +Reference - `D* search Wikipedia `__ @@ -74,7 +74,7 @@ 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: +Reference - `Improved Fast Replanning for Robot Navigation in Unknown Terrain `_ @@ -88,7 +88,7 @@ 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 `__ diff --git a/docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst b/docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst index 463363ddcf..1c5df1c9cc 100644 --- a/docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst +++ b/docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst @@ -16,7 +16,7 @@ Lookup table generation sample .. image:: lookup_table.png -Ref: +Reference - `Optimal rough terrain trajectory generation for wheeled mobile robots `__ diff --git a/docs/modules/5_path_planning/prm_planner/prm_planner_main.rst b/docs/modules/5_path_planning/prm_planner/prm_planner_main.rst index 0628719176..f816c5c1b9 100644 --- a/docs/modules/5_path_planning/prm_planner/prm_planner_main.rst +++ b/docs/modules/5_path_planning/prm_planner/prm_planner_main.rst @@ -13,7 +13,7 @@ Cyan crosses means searched points with Dijkstra method, The red line is the final path of PRM. -Ref: +Reference - `Probabilistic roadmap - Wikipedia `__ diff --git a/docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst b/docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst index 0412b3c9b3..66c3001c05 100644 --- a/docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst +++ b/docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst @@ -97,7 +97,7 @@ Each velocity and acceleration boundary condition can be calculated with each or :math:`v_{xe}=v_ecos(\theta_e), v_{ye}=v_esin(\theta_e)` -References: +Reference ~~~~~~~~~~~ - `Local Path Planning And Motion Control For Agv In diff --git a/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst b/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst index ff377eb91b..a4a5d28e01 100644 --- a/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst +++ b/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst @@ -380,7 +380,7 @@ Hence, we have: - :math:`v = (t - φ)` -Ref: +Reference - `15.3.2 Reeds-Shepp Curves `__ diff --git a/docs/modules/5_path_planning/rrt/rrt_main.rst b/docs/modules/5_path_planning/rrt/rrt_main.rst index e5f351a7ba..da3a4957a5 100644 --- a/docs/modules/5_path_planning/rrt/rrt_main.rst +++ b/docs/modules/5_path_planning/rrt/rrt_main.rst @@ -53,7 +53,7 @@ This is a path planning code with Informed RRT*. The cyan ellipse is the heuristic sampling domain of Informed RRT*. -Ref: +Reference - `Informed RRT\*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal @@ -68,7 +68,7 @@ Batch Informed RRT\* This is a path planning code with Batch Informed RRT*. -Ref: +Reference - `Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric @@ -87,7 +87,7 @@ In this code, pure-pursuit algorithm is used for steering control, PID is used for speed control. -Ref: +Reference - `Motion Planning in Complex Environments using Closed-loop Prediction `__ @@ -109,7 +109,7 @@ A double integrator motion model is used for LQR local planner. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRRRTStar/animation.gif -Ref: +Reference - `LQR-RRT\*: Optimal Sampling-Based Motion Planning with Automatically Derived Extension diff --git a/docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst b/docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst index d5e7ed17d1..bf89ac11ae 100644 --- a/docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst +++ b/docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst @@ -22,7 +22,7 @@ Lane sampling .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/LaneSampling.gif -Ref: +Reference - `Optimal rough terrain trajectory generation for wheeled mobile robots `__ diff --git a/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst b/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst index 92e729ab29..65e0e53465 100644 --- a/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst +++ b/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst @@ -11,7 +11,7 @@ Cyan crosses mean searched points with Dijkstra method, The red line is the final path of Vornoi Road-Map. -Ref: +Reference - `Robotic Motion Planning `__ diff --git a/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst b/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst index ded187e972..568ef9a0df 100644 --- a/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst +++ b/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst @@ -134,7 +134,7 @@ Simulation results -References: +Reference ~~~~~~~~~~~ - `Towards fully autonomous driving: Systems and algorithms `__ diff --git a/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst b/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst index baca7a33fc..831423f364 100644 --- a/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst +++ b/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst @@ -113,7 +113,7 @@ The optimal control input `u` can be calculated as: where `K` is the feedback gain matrix obtained by solving the Riccati equation. -References: +Reference ~~~~~~~~~~~ - `ApolloAuto/apollo: An open autonomous driving platform `_ diff --git a/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst b/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst index 5c7bcef85f..d7354cf8fb 100644 --- a/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst +++ b/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst @@ -9,7 +9,7 @@ speed control. The red line is a target course, the green cross means the target point for pure pursuit control, the blue line is the tracking. -References: +Reference ~~~~~~~~~~~ - `A Survey of Motion Planning and Control Techniques for Self-driving diff --git a/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst b/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst index 70875fdc6c..d18cd6fbf7 100644 --- a/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst +++ b/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst @@ -6,7 +6,7 @@ PID speed control. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/rear_wheel_feedback/animation.gif -References: +Reference ~~~~~~~~~~~ - `A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles `__ diff --git a/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst b/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst index fe325b0102..69089ac33b 100644 --- a/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst +++ b/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst @@ -6,7 +6,7 @@ control. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/stanley_controller/animation.gif -References: +Reference ~~~~~~~~~~~ - `Stanley: The robot that won the DARPA grand diff --git a/users_comments.md b/users_comments.md index a2f798eac4..1674f21377 100644 --- a/users_comments.md +++ b/users_comments.md @@ -10,7 +10,7 @@ This is an electric wheelchair control demo by [Katsushun89](https://github.com/ ![1](https://github.com/AtsushiSakai/PythonRoboticsGifs/blob/master/Users/WHILL_Model_CR_with_move_to_a_pose/WHLL_Model_CR_with_move_to_a_pose.gif) -Ref: +Reference: - [toioと同じように動くWHILL Model CR (in Japanese)](https://qiita.com/KatsuShun89/items/68fde7544ecfe36096d2) From 5392fcff4d555421b4417827dc67c611ad14cc06 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 3 May 2025 09:25:12 +0900 Subject: [PATCH 07/81] Refactor module links and improve code documentation. (#1211) * Refactor module links and improve code documentation. Updated documentation to rename "API" sections to "Code Link" for clarity and consistency. Enhanced docstrings for `circle_fitting` and `kmeans_clustering` functions, improving parameter descriptions and adding return value details. Fixed typos in function and file names in the ray casting grid map module. * Fix import typo in ray casting grid map test module. Corrected the import statement in the test file by updating the module's name to `ray_casting_grid_map` for consistency with the source file. This ensures proper functionality of the test suite. --- Mapping/circle_fitting/circle_fitting.py | 33 +++++++++++++++---- .../kmeans_clustering/kmeans_clustering.py | 27 ++++++++++++++- .../ray_casting_grid_map.py} | 4 +-- .../circle_fitting/circle_fitting_main.rst | 4 +++ .../distance_map/distance_map_main.rst | 4 +-- .../gaussian_grid_map_main.rst | 6 ++++ .../k_means_object_clustering_main.rst | 6 ++++ .../lidar_to_grid_map_tutorial_main.rst | 6 ++++ .../normal_vector_estimation_main.rst | 8 ++--- .../point_cloud_sampling_main.rst | 8 ++--- .../ray_casting_grid_map_main.rst | 7 +++- .../rectangle_fitting_main.rst | 4 +-- ...id_map.py => test_ray_casting_grid_map.py} | 2 +- 13 files changed, 96 insertions(+), 23 deletions(-) rename Mapping/{raycasting_grid_map/raycasting_grid_map.py => ray_casting_grid_map/ray_casting_grid_map.py} (96%) rename tests/{test_raycasting_grid_map.py => test_ray_casting_grid_map.py} (71%) diff --git a/Mapping/circle_fitting/circle_fitting.py b/Mapping/circle_fitting/circle_fitting.py index 2eba550127..b5714b507c 100644 --- a/Mapping/circle_fitting/circle_fitting.py +++ b/Mapping/circle_fitting/circle_fitting.py @@ -16,12 +16,33 @@ def circle_fitting(x, y): """ - Circle Fitting with least squared - input: point x-y positions - output cxe x center position - cye y center position - re radius of circle - error: prediction error + Fits a circle to a given set of points using a least-squares approach. + + This function calculates the center (x, y) and radius of a circle that best fits + the given set of points in a two-dimensional plane. It minimizes the squared + errors between the circle and the provided points and returns the calculated + center coordinates, radius, and the fitting error. + + Raises + ------ + ValueError + If the input lists x and y do not contain the same number of elements. + + Parameters + ---------- + x : list[float] + The x-coordinates of the points. + y : list[float] + The y-coordinates of the points. + + Returns + ------- + tuple[float, float, float, float] + A tuple containing: + - The x-coordinate of the center of the fitted circle (float). + - The y-coordinate of the center of the fitted circle (float). + - The radius of the fitted circle (float). + - The fitting error as a deviation metric (float). """ sumx = sum(x) diff --git a/Mapping/kmeans_clustering/kmeans_clustering.py b/Mapping/kmeans_clustering/kmeans_clustering.py index e18960e990..cee01e5ad5 100644 --- a/Mapping/kmeans_clustering/kmeans_clustering.py +++ b/Mapping/kmeans_clustering/kmeans_clustering.py @@ -17,12 +17,37 @@ def kmeans_clustering(rx, ry, nc): + """ + Performs k-means clustering on the given dataset, iteratively adjusting cluster centroids + until convergence within a defined threshold or reaching the maximum number of + iterations. + + The implementation initializes clusters, calculates initial centroids, and refines the + clusters through iterative updates to optimize the cost function based on minimum + distance between datapoints and centroids. + + Arguments: + rx: List[float] + The x-coordinates of the dataset points to be clustered. + ry: List[float] + The y-coordinates of the dataset points to be clustered. + nc: int + The number of clusters to group the data into. + + Returns: + Clusters + An instance containing the final cluster assignments and centroids after + convergence. + + Raises: + None + + """ clusters = Clusters(rx, ry, nc) clusters.calc_centroid() pre_cost = float("inf") for loop in range(MAX_LOOP): - print("loop:", loop) cost = clusters.update_clusters() clusters.calc_centroid() diff --git a/Mapping/raycasting_grid_map/raycasting_grid_map.py b/Mapping/ray_casting_grid_map/ray_casting_grid_map.py similarity index 96% rename from Mapping/raycasting_grid_map/raycasting_grid_map.py rename to Mapping/ray_casting_grid_map/ray_casting_grid_map.py index 8ce37b925b..c7e73f0630 100644 --- a/Mapping/raycasting_grid_map/raycasting_grid_map.py +++ b/Mapping/ray_casting_grid_map/ray_casting_grid_map.py @@ -48,7 +48,7 @@ def atan_zero_to_twopi(y, x): return angle -def precasting(minx, miny, xw, yw, xyreso, yawreso): +def pre_casting(minx, miny, xw, yw, xyreso, yawreso): precast = [[] for i in range(int(round((math.pi * 2.0) / yawreso)) + 1)] @@ -81,7 +81,7 @@ def generate_ray_casting_grid_map(ox, oy, xyreso, yawreso): pmap = [[0.0 for i in range(yw)] for i in range(xw)] - precast = precasting(minx, miny, xw, yw, xyreso, yawreso) + precast = pre_casting(minx, miny, xw, yw, xyreso, yawreso) for (x, y) in zip(ox, oy): diff --git a/docs/modules/3_mapping/circle_fitting/circle_fitting_main.rst b/docs/modules/3_mapping/circle_fitting/circle_fitting_main.rst index 1892d1f8f7..e243529a9c 100644 --- a/docs/modules/3_mapping/circle_fitting/circle_fitting_main.rst +++ b/docs/modules/3_mapping/circle_fitting/circle_fitting_main.rst @@ -11,3 +11,7 @@ The red crosses are observations from a ranging sensor. The red circle is the estimated object shape using circle fitting. +Code Link +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: Mapping.circle_fitting.circle_fitting.circle_fitting diff --git a/docs/modules/3_mapping/distance_map/distance_map_main.rst b/docs/modules/3_mapping/distance_map/distance_map_main.rst index 0ef9e3022f..ec60e752c9 100644 --- a/docs/modules/3_mapping/distance_map/distance_map_main.rst +++ b/docs/modules/3_mapping/distance_map/distance_map_main.rst @@ -14,8 +14,8 @@ The algorithm is demonstrated on a simple 2D grid with obstacles: .. image:: distance_map.png -API -~~~ +Code Link +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. autofunction:: Mapping.DistanceMap.distance_map.compute_sdf diff --git a/docs/modules/3_mapping/gaussian_grid_map/gaussian_grid_map_main.rst b/docs/modules/3_mapping/gaussian_grid_map/gaussian_grid_map_main.rst index b0f112a871..50033d2a20 100644 --- a/docs/modules/3_mapping/gaussian_grid_map/gaussian_grid_map_main.rst +++ b/docs/modules/3_mapping/gaussian_grid_map/gaussian_grid_map_main.rst @@ -6,3 +6,9 @@ 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 + +Code Link +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: Mapping.gaussian_grid_map.gaussian_grid_map.generate_gaussian_grid_map + diff --git a/docs/modules/3_mapping/k_means_object_clustering/k_means_object_clustering_main.rst b/docs/modules/3_mapping/k_means_object_clustering/k_means_object_clustering_main.rst index e098ca5409..0ece604009 100644 --- a/docs/modules/3_mapping/k_means_object_clustering/k_means_object_clustering_main.rst +++ b/docs/modules/3_mapping/k_means_object_clustering/k_means_object_clustering_main.rst @@ -4,3 +4,9 @@ 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 + +Code Link +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: Mapping.kmeans_clustering.kmeans_clustering.kmeans_clustering + diff --git a/docs/modules/3_mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst b/docs/modules/3_mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst index 1f62179efd..29f5878e48 100644 --- a/docs/modules/3_mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst +++ b/docs/modules/3_mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst @@ -196,3 +196,9 @@ Let’s use this flood fill on real data: .. image:: lidar_to_grid_map_tutorial_14_1.png +Code Link +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: Mapping.lidar_to_grid_map.lidar_to_grid_map.main + + diff --git a/docs/modules/3_mapping/normal_vector_estimation/normal_vector_estimation_main.rst b/docs/modules/3_mapping/normal_vector_estimation/normal_vector_estimation_main.rst index a4d1bf0df2..68a19e1534 100644 --- a/docs/modules/3_mapping/normal_vector_estimation/normal_vector_estimation_main.rst +++ b/docs/modules/3_mapping/normal_vector_estimation/normal_vector_estimation_main.rst @@ -25,8 +25,8 @@ This is an example of normal vector calculation: .. figure:: normal_vector_calc.png -API -===== +Code Link +========== .. autofunction:: Mapping.normal_vector_estimation.normal_vector_estimation.calc_normal_vector @@ -67,8 +67,8 @@ This is an example of RANSAC based normal vector estimation: .. figure:: ransac_normal_vector_estimation.png -API -===== +Code Link +========== .. autofunction:: Mapping.normal_vector_estimation.normal_vector_estimation.ransac_normal_vector_estimation diff --git a/docs/modules/3_mapping/point_cloud_sampling/point_cloud_sampling_main.rst b/docs/modules/3_mapping/point_cloud_sampling/point_cloud_sampling_main.rst index cbb5652f56..8cb08d4b78 100644 --- a/docs/modules/3_mapping/point_cloud_sampling/point_cloud_sampling_main.rst +++ b/docs/modules/3_mapping/point_cloud_sampling/point_cloud_sampling_main.rst @@ -27,8 +27,8 @@ This method determines which each point is in a grid, and replaces the point clouds that are in the same Voxel with their average to reduce the number of points. -API -===== +Code Link +========== .. autofunction:: Mapping.point_cloud_sampling.point_cloud_sampling.voxel_point_sampling @@ -61,8 +61,8 @@ Although this method does not have good performance comparing the Farthest distance sample where each point is distributed farther from each other, this is suitable for real-time processing because of its fast computation time. -API -===== +Code Link +========== .. autofunction:: Mapping.point_cloud_sampling.point_cloud_sampling.poisson_disk_sampling diff --git a/docs/modules/3_mapping/ray_casting_grid_map/ray_casting_grid_map_main.rst b/docs/modules/3_mapping/ray_casting_grid_map/ray_casting_grid_map_main.rst index cc5a1a1c5b..bee2f64193 100644 --- a/docs/modules/3_mapping/ray_casting_grid_map/ray_casting_grid_map_main.rst +++ b/docs/modules/3_mapping/ray_casting_grid_map/ray_casting_grid_map_main.rst @@ -3,4 +3,9 @@ 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 \ No newline at end of file +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/raycasting_grid_map/animation.gif + +Code Link +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: Mapping.ray_casting_grid_map.ray_casting_grid_map.generate_ray_casting_grid_map diff --git a/docs/modules/3_mapping/rectangle_fitting/rectangle_fitting_main.rst b/docs/modules/3_mapping/rectangle_fitting/rectangle_fitting_main.rst index b6ced1dc1d..06d85efe61 100644 --- a/docs/modules/3_mapping/rectangle_fitting/rectangle_fitting_main.rst +++ b/docs/modules/3_mapping/rectangle_fitting/rectangle_fitting_main.rst @@ -57,8 +57,8 @@ This evaluation function uses the squreed distances between the edges of the rec Calculating the squared error is the same as calculating the variance. The smaller this variance, the more it signifies that the points fit within the rectangle. -API -~~~~~~ +Code Link +~~~~~~~~~~~ .. autoclass:: Mapping.rectangle_fitting.rectangle_fitting.LShapeFitting :members: diff --git a/tests/test_raycasting_grid_map.py b/tests/test_ray_casting_grid_map.py similarity index 71% rename from tests/test_raycasting_grid_map.py rename to tests/test_ray_casting_grid_map.py index f08ae9277e..2d192c9310 100644 --- a/tests/test_raycasting_grid_map.py +++ b/tests/test_ray_casting_grid_map.py @@ -1,5 +1,5 @@ import conftest # Add root path to sys.path -from Mapping.raycasting_grid_map import raycasting_grid_map as m +from Mapping.ray_casting_grid_map import ray_casting_grid_map as m def test1(): From e1cdb24ecfd68ff4eaf51626e5a3f4854da588a4 Mon Sep 17 00:00:00 2001 From: Yuri Harada <55676955+NightzDev@users.noreply.github.com> Date: Fri, 2 May 2025 21:51:20 -0300 Subject: [PATCH 08/81] docs: rewrite internal sensors section with detailed descriptions and references (#1197) * docs: rewrite and enhance internal sensors documentation * Update docs/modules/12_appendix/internal_sensors_main.rst --------- Co-authored-by: Atsushi Sakai --- .../12_appendix/internal_sensors_main.rst | 41 +++++++++++++++---- 1 file changed, 34 insertions(+), 7 deletions(-) diff --git a/docs/modules/12_appendix/internal_sensors_main.rst b/docs/modules/12_appendix/internal_sensors_main.rst index 18f209098e..fa2594a2bf 100644 --- a/docs/modules/12_appendix/internal_sensors_main.rst +++ b/docs/modules/12_appendix/internal_sensors_main.rst @@ -3,32 +3,59 @@ Internal Sensors for Robots ============================ -This project, `PythonRobotics`, focuses on algorithms, so hardware is not included. -However, having basic knowledge of hardware in robotics is also important for understanding algorithms. -Therefore, we will provide an overview. +This project, `PythonRobotics`, focuses on algorithms, so hardware is not included. However, having basic knowledge of hardware in robotics is also important for understanding algorithms. Therefore, we will provide an overview. Introduction ------------- +------------- + +In robotic systems, internal sensors play a crucial role in monitoring the robot’s internal state, such as orientation, acceleration, angular velocity, altitude, and temperature. These sensors provide essential feedback that supports control, localization, and safety mechanisms. While external sensors perceive the environment, internal sensors give the robot self-awareness of its own motion and condition. Understanding the principles and characteristics of these sensors is vital to fully utilize their information within algorithms and decision-making systems. This section outlines the main internal sensors used in robotics. Global Navigation Satellite System (GNSS) -------------------------------------------- +----------------------------------------- + +GNSS is a satellite-based navigation system that provides global positioning and time information by analyzing signals from multiple satellites. It is commonly used in outdoor environments for absolute positioning. Although GNSS offers global coverage without infrastructure dependency, its performance is limited indoors or in obstructed areas, and it suffers from low update rates and susceptibility to signal noise. It is widely used in outdoor navigation for drones, vehicles, and delivery robots. Gyroscope ---------- +A gyroscope measures angular velocity around the robot’s axes, enabling orientation and attitude estimation through detection of the Coriolis effect. Gyroscopes are compact, cost-effective, and provide high update rates, but they are prone to drift and require calibration or sensor fusion for long-term accuracy. These sensors are essential in drones, balancing robots, and IMU-based systems for motion tracking. + Accelerometer --------------- +--------------- + +An accelerometer measures linear acceleration along one or more axes, typically by detecting mass displacement due to motion. It is small, affordable, and useful for detecting movement, tilt, or vibration. However, accelerometers are limited by noisy output and cannot independently determine position without integration and fusion. They are commonly applied in wearable robotics, step counters, and vibration sensing. Magnetometer -------------- +A magnetometer measures the direction and intensity of magnetic fields, enabling heading estimation based on Earth’s magnetic field. It is lightweight and useful for orientation, especially in GPS-denied environments, though it is sensitive to interference from electronics and requires calibration. Magnetometers are often used in conjunction with IMUs for navigation and directional awareness. + Inertial Measurement Unit (IMU) -------------------------------- +An IMU integrates a gyroscope, accelerometer, and sometimes a magnetometer to estimate a robot's motion and orientation through sensor fusion techniques such as Kalman filters. IMUs are compact and provide high-frequency data, which is essential for localization and navigation in GPS-denied areas. Nonetheless, they accumulate drift over time and require complex filtering to maintain accuracy. IMUs are found in drones, mobile robots, and motion tracking systems. + Pressure Sensor ------------------ +---------------- + +Pressure sensors detect atmospheric or fluid pressure by measuring the force exerted on a diaphragm. They are effective for estimating altitude and monitoring environmental conditions, especially in drones and underwater robots. Although cost-effective and efficient, their accuracy may degrade due to temperature variation and limitations in low-altitude resolution. Temperature Sensor -------------------- +Temperature sensors monitor environmental or internal component temperatures, using changes in resistance or voltage depending on sensor type (e.g., RTD or thermocouple). They are simple and reliable for safety and thermal regulation, though they may respond slowly and be affected by nearby electronics. Common applications include battery and motor monitoring, safety systems, and ambient sensing. + +References +---------- +- *Introduction to Autonomous Mobile Robots*, Roland Siegwart, Illah Nourbakhsh, Davide Scaramuzza +- *Probabilistic Robotics*, Sebastian Thrun, Wolfram Burgard, Dieter Fox +- Wikipedia articles: + + - `Inertial Measurement Unit (IMU) `_ + - `Accelerometer `_ + - `Gyroscope `_ + - `Magnetometer `_ + - `Pressure sensor `_ + - `Temperature sensor `_ +- `Adafruit Sensor Guides `_ \ No newline at end of file From d2fe5ae8f0e414f394c00570771c07807d167e68 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 4 May 2025 09:50:49 +0900 Subject: [PATCH 09/81] Add code links to SLAM module documentation (#1212) This commit updates SLAM module documentation files to include direct code links for better navigation. It also adjusts imports in `test_iterative_closest_point.py` to reflect updated module structure and adds a safety check for the directory in `runtests.sh`. --- .../icp_matching.py} | 0 docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst | 5 +++++ docs/modules/4_slam/FastSLAM2/FastSLAM2_main.rst | 6 ++++++ docs/modules/4_slam/ekf_slam/ekf_slam_main.rst | 6 ++++++ docs/modules/4_slam/graph_slam/graph_slam_main.rst | 6 ++++++ .../iterative_closest_point_matching_main.rst | 8 +++++++- runtests.sh | 1 + tests/test_iterative_closest_point.py | 2 +- 8 files changed, 32 insertions(+), 2 deletions(-) rename SLAM/{iterative_closest_point/iterative_closest_point.py => ICPMatching/icp_matching.py} (100%) diff --git a/SLAM/iterative_closest_point/iterative_closest_point.py b/SLAM/ICPMatching/icp_matching.py similarity index 100% rename from SLAM/iterative_closest_point/iterative_closest_point.py rename to SLAM/ICPMatching/icp_matching.py diff --git a/docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst b/docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst index a2aa521216..b6bafa0982 100644 --- a/docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst +++ b/docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst @@ -22,6 +22,11 @@ The red points are particles of FastSLAM. Black points are landmarks, blue crosses are estimated landmark positions by FastSLAM. +Code Link +~~~~~~~~~~~~ + +.. autofunction:: SLAM.FastSLAM1.fast_slam1.fast_slam1 + Introduction ~~~~~~~~~~~~ diff --git a/docs/modules/4_slam/FastSLAM2/FastSLAM2_main.rst b/docs/modules/4_slam/FastSLAM2/FastSLAM2_main.rst index 9e79b496a3..59ed3b9f75 100644 --- a/docs/modules/4_slam/FastSLAM2/FastSLAM2_main.rst +++ b/docs/modules/4_slam/FastSLAM2/FastSLAM2_main.rst @@ -7,6 +7,12 @@ The animation has the same meanings as one of FastSLAM 1.0. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM2/animation.gif +Code Link +~~~~~~~~~~~ + +.. autofunction:: SLAM.FastSLAM2.fast_slam2.fast_slam2 + + References ~~~~~~~~~~ diff --git a/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst b/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst index a1486ffe1e..3967a7ae4d 100644 --- a/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst +++ b/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst @@ -21,6 +21,12 @@ This is a simulation of EKF SLAM. - Blue line: ground truth - Red line: EKF SLAM position estimation +Code Link +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: SLAM.EKFSLAM.ekf_slam.ekf_slam + + Introduction ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/modules/4_slam/graph_slam/graph_slam_main.rst b/docs/modules/4_slam/graph_slam/graph_slam_main.rst index a2ef513527..2ef17e4179 100644 --- a/docs/modules/4_slam/graph_slam/graph_slam_main.rst +++ b/docs/modules/4_slam/graph_slam/graph_slam_main.rst @@ -13,6 +13,12 @@ The black stars are landmarks for graph edge generation. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/GraphBasedSLAM/animation.gif +Code Link +~~~~~~~~~~~~ + +.. autofunction:: SLAM.GraphBasedSLAM.graph_based_slam.graph_based_slam + + .. include:: graphSLAM_doc.rst .. include:: graphSLAM_formulation.rst .. include:: graphSLAM_SE2_example.rst diff --git a/docs/modules/4_slam/iterative_closest_point_matching/iterative_closest_point_matching_main.rst b/docs/modules/4_slam/iterative_closest_point_matching/iterative_closest_point_matching_main.rst index a30b1fc99b..772fe62889 100644 --- a/docs/modules/4_slam/iterative_closest_point_matching/iterative_closest_point_matching_main.rst +++ b/docs/modules/4_slam/iterative_closest_point_matching/iterative_closest_point_matching_main.rst @@ -10,7 +10,13 @@ points to points. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/iterative_closest_point/animation.gif +Code Link +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: SLAM.ICPMatching.icp_matching.icp_matching + + References -~~~~~~~~~~ +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - `Introduction to Mobile Robotics: Iterative Closest Point Algorithm `_ diff --git a/runtests.sh b/runtests.sh index 12d1b80453..c4460c8aa7 100755 --- a/runtests.sh +++ b/runtests.sh @@ -1,4 +1,5 @@ #!/usr/bin/env bash +cd "$(dirname "$0")" || exit 1 echo "Run test suites! " # === pytest based test runner === diff --git a/tests/test_iterative_closest_point.py b/tests/test_iterative_closest_point.py index 3e726b5649..cdfa89cc55 100644 --- a/tests/test_iterative_closest_point.py +++ b/tests/test_iterative_closest_point.py @@ -1,5 +1,5 @@ import conftest -from SLAM.iterative_closest_point import iterative_closest_point as m +from SLAM.ICPMatching import icp_matching as m def test_1(): From a38da41bafaf82bb56d4491d94a0095c1019ac4a Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 4 May 2025 20:32:11 +0900 Subject: [PATCH 10/81] Add code links to documentation and fix naming inconsistencies (#1213) Added references to related Python functions in documentation for better navigation and usability. Corrected inconsistencies in module and test names to align with their respective directories and improve clarity. --- .../rear_wheel_feedback_control.py} | 0 .../stanley_control.py} | 0 .../10_inverted_pendulum/inverted_pendulum_main.rst | 12 ++++++++++++ .../behavior_tree/behavior_tree_main.rst | 2 +- .../state_machine/state_machine_main.rst | 4 ++-- .../cgmres_nmpc/cgmres_nmpc_main.rst | 5 +++++ .../lqr_speed_and_steering_control_main.rst | 7 ++++++- .../lqr_steering_control_main.rst | 6 +++++- ...l_predictive_speed_and_steering_control_main.rst | 13 ++++++------- .../move_to_a_pose_main.rst} | 8 +++++++- docs/modules/6_path_tracking/path_tracking_main.rst | 2 +- .../pure_pursuit_tracking_main.rst | 6 ++++++ .../rear_wheel_feedback_control_main.rst | 6 ++++++ .../stanley_control/stanley_control_main.rst | 6 ++++++ .../n_joint_arm_to_point_control_main.rst | 7 +++++++ .../obstacle_avoidance_arm_navigation_main.rst | 7 ++++++- .../7_arm_navigation/planar_two_link_ik_main.rst | 6 ++++++ .../drone_3d_trajectory_following_main.rst | 5 +++++ .../rocket_powered_landing_main.rst | 8 ++++++++ .../bipedal_planner/bipedal_planner_main.rst | 5 +++++ tests/test_rear_wheel_feedback.py | 2 +- tests/test_stanley_controller.py | 2 +- 22 files changed, 102 insertions(+), 17 deletions(-) rename PathTracking/{rear_wheel_feedback/rear_wheel_feedback.py => rear_wheel_feedback_control/rear_wheel_feedback_control.py} (100%) rename PathTracking/{stanley_controller/stanley_controller.py => stanley_control/stanley_control.py} (100%) rename docs/modules/6_path_tracking/{move_to_a_pose_control/move_to_a_pose_control_main.rst => move_to_a_pose/move_to_a_pose_main.rst} (97%) diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback_control/rear_wheel_feedback_control.py similarity index 100% rename from PathTracking/rear_wheel_feedback/rear_wheel_feedback.py rename to PathTracking/rear_wheel_feedback_control/rear_wheel_feedback_control.py diff --git a/PathTracking/stanley_controller/stanley_controller.py b/PathTracking/stanley_control/stanley_control.py similarity index 100% rename from PathTracking/stanley_controller/stanley_controller.py rename to PathTracking/stanley_control/stanley_control.py diff --git a/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst b/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst index 048cbea9ac..58dc0f2e57 100644 --- a/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst +++ b/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst @@ -89,6 +89,12 @@ and :math:`P` is the unique positive definite solution to the discrete time .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/InvertedPendulumCart/animation_lqr.gif +Code Link +^^^^^^^^^^^ + +.. autofunction:: InvertedPendulum.inverted_pendulum_lqr_control.main + + MPC control ~~~~~~~~~~~~~~~~~~~~~~~~~~~ The MPC controller minimize this cost function defined as: @@ -101,3 +107,9 @@ subject to: - Initial state .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/InvertedPendulumCart/animation.gif + +Code Link +^^^^^^^^^^^ + +.. autofunction:: InvertedPendulum.inverted_pendulum_mpc_control.main + diff --git a/docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst b/docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst index ae3e16da81..22849f7c54 100644 --- a/docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst +++ b/docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst @@ -5,7 +5,7 @@ Behavior Tree is a modular, hierarchical decision model that is widely used in r It present some similarities to hierarchical state machines with the key difference that the main building block of a behavior is a task rather than a state. Behavior Tree have been shown to generalize several other control architectures (https://ieeexplore.ieee.org/document/7790863) -Core Concepts +Code Link ~~~~~~~~~~~~~ Control Node diff --git a/docs/modules/13_mission_planning/state_machine/state_machine_main.rst b/docs/modules/13_mission_planning/state_machine/state_machine_main.rst index abaece1b11..3f516d46a9 100644 --- a/docs/modules/13_mission_planning/state_machine/state_machine_main.rst +++ b/docs/modules/13_mission_planning/state_machine/state_machine_main.rst @@ -12,8 +12,8 @@ Core Concepts - **Action**: An operation executed during transition (before entering new state) - **Guard**: A precondition that must be satisfied to allow transition -API -~~~ +Code Link +~~~~~~~~~~~ .. autoclass:: MissionPlanning.StateMachine.state_machine.StateMachine :members: add_transition, process, register_state diff --git a/docs/modules/6_path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst b/docs/modules/6_path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst index 23f1218a94..914a4555ff 100644 --- a/docs/modules/6_path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst +++ b/docs/modules/6_path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst @@ -17,6 +17,11 @@ Nonlinear Model Predictive Control with C-GMRES .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/cgmres_nmpc/animation.gif :alt: gif +Code Link +~~~~~~~~~~~~ + +.. autofunction:: PathTracking.cgmres_nmpc.cgmres_nmpc.NMPCControllerCGMRES + Mathematical Formulation ~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst b/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst index 568ef9a0df..b0ba9e0d45 100644 --- a/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst +++ b/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst @@ -7,7 +7,12 @@ Path tracking simulation with LQR speed and steering control. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_speed_steer_control/animation.gif -`[Code Link] `_ + +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: PathTracking.lqr_speed_steer_control.lqr_speed_steer_control.lqr_speed_steering_control + Overview ~~~~~~~~ diff --git a/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst b/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst index 831423f364..fc8f2a33aa 100644 --- a/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst +++ b/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst @@ -8,7 +8,11 @@ control. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_steer_control/animation.gif -`[Code Link] `_ +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: PathTracking.lqr_steer_control.lqr_steer_control.lqr_steering_control + Overview ~~~~~~~~ diff --git a/docs/modules/6_path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst b/docs/modules/6_path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst index de55b545ba..76a6819a46 100644 --- a/docs/modules/6_path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst +++ b/docs/modules/6_path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst @@ -5,13 +5,6 @@ Model predictive speed and steering control .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/model_predictive_speed_and_steer_control/animation.gif?raw=true :alt: Model predictive speed and steering control - Model predictive speed and steering control - -code: - -`PythonRobotics/model_predictive_speed_and_steer_control.py at master · -AtsushiSakai/PythonRobotics `__ - This is a path tracking simulation using model predictive control (MPC). The MPC controller controls vehicle speed and steering base on @@ -22,6 +15,12 @@ This code uses cvxpy as an optimization modeling tool. - `Welcome to CVXPY 1.0 — CVXPY 1.0.6 documentation `__ +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: PathTracking.model_predictive_speed_and_steer_control.model_predictive_speed_and_steer_control.iterative_linear_mpc_control + + MPC modeling ~~~~~~~~~~~~ diff --git a/docs/modules/6_path_tracking/move_to_a_pose_control/move_to_a_pose_control_main.rst b/docs/modules/6_path_tracking/move_to_a_pose/move_to_a_pose_main.rst similarity index 97% rename from docs/modules/6_path_tracking/move_to_a_pose_control/move_to_a_pose_control_main.rst rename to docs/modules/6_path_tracking/move_to_a_pose/move_to_a_pose_main.rst index 77ec682a30..19bb0e4c14 100644 --- a/docs/modules/6_path_tracking/move_to_a_pose_control/move_to_a_pose_control_main.rst +++ b/docs/modules/6_path_tracking/move_to_a_pose/move_to_a_pose_main.rst @@ -3,7 +3,13 @@ Move to a Pose Control In this section, we present the logic of PathFinderController that drives a car from a start pose (x, y, theta) to a goal pose. A simulation of moving to a pose control is presented below. -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/move_to_pose/animation.gif + +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: PathTracking.move_to_pose.move_to_pose.move_to_pose + Position Control of non-Holonomic Systems ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/modules/6_path_tracking/path_tracking_main.rst b/docs/modules/6_path_tracking/path_tracking_main.rst index d98e324583..742cc807e6 100644 --- a/docs/modules/6_path_tracking/path_tracking_main.rst +++ b/docs/modules/6_path_tracking/path_tracking_main.rst @@ -16,4 +16,4 @@ Path tracking is the ability of a robot to follow the reference path generated b lqr_speed_and_steering_control/lqr_speed_and_steering_control model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control cgmres_nmpc/cgmres_nmpc - move_to_a_pose_control/move_to_a_pose_control + move_to_a_pose/move_to_a_pose diff --git a/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst b/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst index d7354cf8fb..ff6749bbbe 100644 --- a/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst +++ b/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst @@ -9,6 +9,12 @@ speed control. The red line is a target course, the green cross means the target point for pure pursuit control, the blue line is the tracking. +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: PathTracking.pure_pursuit.pure_pursuit.pure_pursuit_steer_control + + Reference ~~~~~~~~~~~ diff --git a/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst b/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst index d18cd6fbf7..56d0db63ad 100644 --- a/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst +++ b/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst @@ -6,6 +6,12 @@ PID speed control. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/rear_wheel_feedback/animation.gif +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: PathTracking.rear_wheel_feedback_control.rear_wheel_feedback_control.rear_wheel_feedback_control + + Reference ~~~~~~~~~~~ - `A Survey of Motion Planning and Control Techniques for Self-driving diff --git a/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst b/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst index 69089ac33b..3c491804f6 100644 --- a/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst +++ b/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst @@ -6,6 +6,12 @@ control. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/stanley_controller/animation.gif +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: PathTracking.stanley_control.stanley_control.stanley_control + + Reference ~~~~~~~~~~~ diff --git a/docs/modules/7_arm_navigation/n_joint_arm_to_point_control_main.rst b/docs/modules/7_arm_navigation/n_joint_arm_to_point_control_main.rst index cc6279f681..56900acde1 100644 --- a/docs/modules/7_arm_navigation/n_joint_arm_to_point_control_main.rst +++ b/docs/modules/7_arm_navigation/n_joint_arm_to_point_control_main.rst @@ -11,3 +11,10 @@ plotting area. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/n_joint_arm_to_point_control/animation.gif In this simulation N = 10, however, you can change it. + + +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: ArmNavigation.n_joint_arm_to_point_control.n_joint_arm_to_point_control.main + diff --git a/docs/modules/7_arm_navigation/obstacle_avoidance_arm_navigation_main.rst b/docs/modules/7_arm_navigation/obstacle_avoidance_arm_navigation_main.rst index 899a64a5cd..4433ab531b 100644 --- a/docs/modules/7_arm_navigation/obstacle_avoidance_arm_navigation_main.rst +++ b/docs/modules/7_arm_navigation/obstacle_avoidance_arm_navigation_main.rst @@ -3,4 +3,9 @@ Arm navigation with obstacle avoidance Arm navigation with obstacle avoidance simulation. -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/arm_obstacle_navigation/animation.gif \ No newline at end of file +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/arm_obstacle_navigation/animation.gif + +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: ArmNavigation.arm_obstacle_navigation.arm_obstacle_navigation.main diff --git a/docs/modules/7_arm_navigation/planar_two_link_ik_main.rst b/docs/modules/7_arm_navigation/planar_two_link_ik_main.rst index 2da2b0dc30..5b2712eb48 100644 --- a/docs/modules/7_arm_navigation/planar_two_link_ik_main.rst +++ b/docs/modules/7_arm_navigation/planar_two_link_ik_main.rst @@ -11,6 +11,12 @@ This is a interactive simulation. You can set the goal position of the end effector with left-click on the plotting area. +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: ArmNavigation.two_joint_arm_to_point_control.two_joint_arm_to_point_control.main + + Inverse Kinematics for a Planar Two-Link Robotic Arm ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/modules/8_aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following_main.rst b/docs/modules/8_aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following_main.rst index c3bdc33cdc..1805bb3f6d 100644 --- a/docs/modules/8_aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following_main.rst +++ b/docs/modules/8_aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following_main.rst @@ -4,3 +4,8 @@ Drone 3d trajectory following This is a 3d trajectory following simulation for a quadrotor. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/AerialNavigation/drone_3d_trajectory_following/animation.gif + +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: AerialNavigation.drone_3d_trajectory_following.drone_3d_trajectory_following.quad_sim diff --git a/docs/modules/8_aerial_navigation/rocket_powered_landing/rocket_powered_landing_main.rst b/docs/modules/8_aerial_navigation/rocket_powered_landing/rocket_powered_landing_main.rst index 6c90d2b44e..4bf5117500 100644 --- a/docs/modules/8_aerial_navigation/rocket_powered_landing/rocket_powered_landing_main.rst +++ b/docs/modules/8_aerial_navigation/rocket_powered_landing/rocket_powered_landing_main.rst @@ -1,6 +1,14 @@ Rocket powered landing ----------------------------- +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/AerialNavigation/rocket_powered_landing/animation.gif + +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: AerialNavigation.rocket_powered_landing.rocket_powered_landing.main + + Simulation ~~~~~~~~~~ diff --git a/docs/modules/9_bipedal/bipedal_planner/bipedal_planner_main.rst b/docs/modules/9_bipedal/bipedal_planner/bipedal_planner_main.rst index 2ee5971e7a..6253715cc1 100644 --- a/docs/modules/9_bipedal/bipedal_planner/bipedal_planner_main.rst +++ b/docs/modules/9_bipedal/bipedal_planner/bipedal_planner_main.rst @@ -4,3 +4,8 @@ Bipedal Planner Bipedal Walking with modifying designated footsteps .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Bipedal/bipedal_planner/animation.gif + +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: Bipedal.bipedal_planner.bipedal_planner.BipedalPlanner diff --git a/tests/test_rear_wheel_feedback.py b/tests/test_rear_wheel_feedback.py index 895eb188b3..eccde52358 100644 --- a/tests/test_rear_wheel_feedback.py +++ b/tests/test_rear_wheel_feedback.py @@ -1,5 +1,5 @@ import conftest # Add root path to sys.path -from PathTracking.rear_wheel_feedback import rear_wheel_feedback as m +from PathTracking.rear_wheel_feedback_control import rear_wheel_feedback_control as m def test1(): diff --git a/tests/test_stanley_controller.py b/tests/test_stanley_controller.py index a1d8073764..bd590cb51a 100644 --- a/tests/test_stanley_controller.py +++ b/tests/test_stanley_controller.py @@ -1,5 +1,5 @@ import conftest # Add root path to sys.path -from PathTracking.stanley_controller import stanley_controller as m +from PathTracking.stanley_control import stanley_control as m def test1(): From 73e1c0bebccf4f4fc67ae59ada53b783a8533096 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 5 May 2025 17:29:45 +0900 Subject: [PATCH 11/81] 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. --- ..._rrtstar.py => batch_informed_rrt_star.py} | 0 PathPlanning/Dijkstra/dijkstra.py | 4 +- .../eta3_spline_trajectory.py | 14 ++--- .../bezier_path/bezier_path_main.rst | 9 ++- .../bspline_path/bspline_path_main.rst | 8 +-- .../bugplanner/bugplanner_main.rst | 8 +++ .../catmull_rom_spline_main.rst | 4 +- .../clothoid_path/clothoid_path_main.rst | 5 ++ .../coverage_path/coverage_path_main.rst | 21 +++++++ .../cubic_spline/cubic_spline_main.rst | 8 +-- .../dubins_path/dubins_path_main.rst | 2 +- .../dynamic_window_approach_main.rst | 12 +++- .../elastic_bands/elastic_bands_main.rst | 7 ++- .../eta3_spline/eta3_spline_main.rst | 7 +++ .../frenet_frame_path_main.rst | 6 ++ .../grid_base_search_main.rst | 50 ++++++++++++++++ .../hybridastar/hybridastar_main.rst | 5 ++ .../lqr_path/lqr_path_main.rst | 5 ++ ...l_predictive_trajectory_generator_main.rst | 7 +++ .../prm_planner/prm_planner_main.rst | 7 +++ .../quintic_polynomials_planner_main.rst | 5 ++ .../reeds_shepp_path_main.rst | 7 +++ .../closed_loop_rrt_star_car/Figure_1.png | Bin .../closed_loop_rrt_star_car/Figure_3.png | Bin .../closed_loop_rrt_star_car/Figure_4.png | Bin .../closed_loop_rrt_star_car/Figure_5.png | Bin docs/modules/5_path_planning/rrt/rrt_main.rst | 54 ++++++++++++++++++ docs/modules/5_path_planning/rrt/rrt_star.rst | 6 ++ .../state_lattice_planner_main.rst | 17 ++++++ .../time_based_grid_search_main.rst | 9 +++ .../visibility_road_map_planner_main.rst | 7 ++- .../vrm_planner/vrm_planner_main.rst | 6 ++ tests/test_batch_informed_rrt_star.py | 2 +- 33 files changed, 277 insertions(+), 25 deletions(-) rename PathPlanning/BatchInformedRRTStar/{batch_informed_rrtstar.py => batch_informed_rrt_star.py} (100%) rename docs/modules/5_path_planning/{ => rrt}/closed_loop_rrt_star_car/Figure_1.png (100%) rename docs/modules/5_path_planning/{ => rrt}/closed_loop_rrt_star_car/Figure_3.png (100%) rename docs/modules/5_path_planning/{ => rrt}/closed_loop_rrt_star_car/Figure_4.png (100%) rename docs/modules/5_path_planning/{ => rrt}/closed_loop_rrt_star_car/Figure_5.png (100%) diff --git a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py b/PathPlanning/BatchInformedRRTStar/batch_informed_rrt_star.py similarity index 100% rename from PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py rename to PathPlanning/BatchInformedRRTStar/batch_informed_rrt_star.py diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py index 8a585e4b18..f5a4703910 100644 --- a/PathPlanning/Dijkstra/dijkstra.py +++ b/PathPlanning/Dijkstra/dijkstra.py @@ -12,7 +12,7 @@ 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 diff --git a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py index a73797dacb..e72d33261e 100644 --- a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py +++ b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py @@ -29,7 +29,7 @@ def __init__(self, actual_vel, max_vel): 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) diff --git a/docs/modules/5_path_planning/bezier_path/bezier_path_main.rst b/docs/modules/5_path_planning/bezier_path/bezier_path_main.rst index 9d9b3de709..19fb89a1b1 100644 --- a/docs/modules/5_path_planning/bezier_path/bezier_path_main.rst +++ b/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 ` + Vehicles `__ diff --git a/docs/modules/5_path_planning/bspline_path/bspline_path_main.rst b/docs/modules/5_path_planning/bspline_path/bspline_path_main.rst index e734352e34..00e5ef2fdb 100644 --- a/docs/modules/5_path_planning/bspline_path/bspline_path_main.rst +++ b/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 diff --git a/docs/modules/5_path_planning/bugplanner/bugplanner_main.rst b/docs/modules/5_path_planning/bugplanner/bugplanner_main.rst index 81c91c0313..e1cd2fe353 100644 --- a/docs/modules/5_path_planning/bugplanner/bugplanner_main.rst +++ b/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 `_ diff --git a/docs/modules/5_path_planning/catmull_rom_spline/catmull_rom_spline_main.rst b/docs/modules/5_path_planning/catmull_rom_spline/catmull_rom_spline_main.rst index 72e558c486..fa2a2ff72b 100644 --- a/docs/modules/5_path_planning/catmull_rom_spline/catmull_rom_spline_main.rst +++ b/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 diff --git a/docs/modules/5_path_planning/clothoid_path/clothoid_path_main.rst b/docs/modules/5_path_planning/clothoid_path/clothoid_path_main.rst index 1e8cee694a..16d0ec03c1 100644 --- a/docs/modules/5_path_planning/clothoid_path/clothoid_path_main.rst +++ b/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 ~~~~~~~~~~ diff --git a/docs/modules/5_path_planning/coverage_path/coverage_path_main.rst b/docs/modules/5_path_planning/coverage_path/coverage_path_main.rst index 0a688b5ed2..eaa876c80b 100644 --- a/docs/modules/5_path_planning/coverage_path/coverage_path_main.rst +++ b/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 `_ @@ -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 `_ diff --git a/docs/modules/5_path_planning/cubic_spline/cubic_spline_main.rst b/docs/modules/5_path_planning/cubic_spline/cubic_spline_main.rst index 33471f7c85..a110217a2e 100644 --- a/docs/modules/5_path_planning/cubic_spline/cubic_spline_main.rst +++ b/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: diff --git a/docs/modules/5_path_planning/dubins_path/dubins_path_main.rst b/docs/modules/5_path_planning/dubins_path/dubins_path_main.rst index 12172fb51e..5a3d14464b 100644 --- a/docs/modules/5_path_planning/dubins_path/dubins_path_main.rst +++ b/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 diff --git a/docs/modules/5_path_planning/dynamic_window_approach/dynamic_window_approach_main.rst b/docs/modules/5_path_planning/dynamic_window_approach/dynamic_window_approach_main.rst index d645838597..ac5322df94 100644 --- a/docs/modules/5_path_planning/dynamic_window_approach/dynamic_window_approach_main.rst +++ b/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 `__ -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DynamicWindowApproach/animation.gif diff --git a/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst b/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst index 8a3e517105..d0109d4ec3 100644 --- a/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst +++ b/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 `__ diff --git a/docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst b/docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst index 82e0a71044..9ee343e8a7 100644 --- a/docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst +++ b/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 `__ diff --git a/docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst b/docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst index 371d536e3f..0f84d381ea 100644 --- a/docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst +++ b/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 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst b/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst index bf82c9f391..c4aa6882aa 100644 --- a/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst +++ b/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 `__ @@ -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 `_ @@ -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 `__ diff --git a/docs/modules/5_path_planning/hybridastar/hybridastar_main.rst b/docs/modules/5_path_planning/hybridastar/hybridastar_main.rst index a9876fa3d4..36f340e0c2 100644 --- a/docs/modules/5_path_planning/hybridastar/hybridastar_main.rst +++ b/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 diff --git a/docs/modules/5_path_planning/lqr_path/lqr_path_main.rst b/docs/modules/5_path_planning/lqr_path/lqr_path_main.rst index 788442ff63..1eb1e4d840 100644 --- a/docs/modules/5_path_planning/lqr_path/lqr_path_main.rst +++ b/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 diff --git a/docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst b/docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst index 1c5df1c9cc..76472a6792 100644 --- a/docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst +++ b/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 `__ diff --git a/docs/modules/5_path_planning/prm_planner/prm_planner_main.rst b/docs/modules/5_path_planning/prm_planner/prm_planner_main.rst index f816c5c1b9..d58d1e2633 100644 --- a/docs/modules/5_path_planning/prm_planner/prm_planner_main.rst +++ b/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 `__ diff --git a/docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst b/docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst index 66c3001c05..c7bc3fb55c 100644 --- a/docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst +++ b/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 diff --git a/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst b/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst index a4a5d28e01..4dd54d7c97 100644 --- a/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst +++ b/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 `__ diff --git a/docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_1.png b/docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_1.png similarity index 100% rename from docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_1.png rename to docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_1.png diff --git a/docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_3.png b/docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_3.png similarity index 100% rename from docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_3.png rename to docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_3.png diff --git a/docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_4.png b/docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_4.png similarity index 100% rename from docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_4.png rename to docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_4.png diff --git a/docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_5.png b/docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_5.png similarity index 100% rename from docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_5.png rename to docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_5.png diff --git a/docs/modules/5_path_planning/rrt/rrt_main.rst b/docs/modules/5_path_planning/rrt/rrt_main.rst index da3a4957a5..1bd5497f4c 100644 --- a/docs/modules/5_path_planning/rrt/rrt_main.rst +++ b/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 `__ + .. _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 `__ @@ -98,6 +144,7 @@ Reference - `[1601.06326] Sampling-based Algorithms for Optimal Motion Planning Using Closed-loop Prediction `__ + .. _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 diff --git a/docs/modules/5_path_planning/rrt/rrt_star.rst b/docs/modules/5_path_planning/rrt/rrt_star.rst index 6deb6b9172..960b9976d5 100644 --- a/docs/modules/5_path_planning/rrt/rrt_star.rst +++ b/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 ^^^^^^^^^^ diff --git a/docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst b/docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst index bf89ac11ae..9f8cc0c50f 100644 --- a/docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst +++ b/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 `__ diff --git a/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst b/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst index 04001d4504..9517e95b31 100644 --- a/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst +++ b/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 ~~~~~~~~~~~ diff --git a/docs/modules/5_path_planning/visibility_road_map_planner/visibility_road_map_planner_main.rst b/docs/modules/5_path_planning/visibility_road_map_planner/visibility_road_map_planner_main.rst index 3c9b7c008c..aac96d6e19 100644 --- a/docs/modules/5_path_planning/visibility_road_map_planner/visibility_road_map_planner_main.rst +++ b/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 `_ diff --git a/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst b/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst index 65e0e53465..a9a41aab74 100644 --- a/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst +++ b/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 `__ diff --git a/tests/test_batch_informed_rrt_star.py b/tests/test_batch_informed_rrt_star.py index 3ad78bdb23..cf0a9827a8 100644 --- a/tests/test_batch_informed_rrt_star.py +++ b/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(): From 0c82dde2be24e5f3955018caa061750d7404978d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 6 May 2025 07:22:51 +0900 Subject: [PATCH 12/81] build(deps): bump ruff from 0.11.7 to 0.11.8 in /requirements (#1215) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.11.7 to 0.11.8. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.11.7...0.11.8) --- updated-dependencies: - dependency-name: ruff dependency-version: 0.11.8 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 88cbb21f81..e78b9b8d51 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -6,4 +6,4 @@ ecos == 2.0.14 pytest == 8.3.5 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.15.0 # For unit test -ruff == 0.11.7 # For unit test +ruff == 0.11.8 # For unit test From 292c9dc613d1b0ace3d25ca8a0295e19549516f2 Mon Sep 17 00:00:00 2001 From: atm <72185056+sutatoruta@users.noreply.github.com> Date: Sat, 10 May 2025 09:34:19 +0900 Subject: [PATCH 13/81] fix broken links (#1216) --- .github/pull_request_template.md | 2 +- README.md | 12 ++++++------ users_comments.md | 2 +- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index b6ac52efa2..c0d9f7eab2 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -1,7 +1,7 @@