mirror of
https://github.com/AtsushiSakai/PythonRobotics.git
synced 2026-01-14 22:48:08 -05:00
Atsushi Sakai
2
.gitignore
vendored
2
.gitignore
vendored
@@ -4,6 +4,8 @@
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
*$py.class
|
||||
_build/
|
||||
.idea/
|
||||
|
||||
# C extensions
|
||||
*.so
|
||||
|
||||
734
README.md
734
README.md
@@ -10,69 +10,7 @@ Python codes for robotics algorithm.
|
||||
* [What is this?](#what-is-this)
|
||||
* [Requirements](#requirements)
|
||||
* [How to use](#how-to-use)
|
||||
* [Localization](#localization)
|
||||
* [Extended Kalman Filter localization](#extended-kalman-filter-localization)
|
||||
* [Unscented Kalman Filter localization](#unscented-kalman-filter-localization)
|
||||
* [Particle filter localization](#particle-filter-localization)
|
||||
* [Histogram filter localization](#histogram-filter-localization)
|
||||
* [Mapping](#mapping)
|
||||
* [Gaussian grid map](#gaussian-grid-map)
|
||||
* [Ray casting grid map](#ray-casting-grid-map)
|
||||
* [k-means object clustering](#k-means-object-clustering)
|
||||
* [Object shape recognition using circle fitting](#object-shape-recognition-using-circle-fitting)
|
||||
* [SLAM](#slam)
|
||||
* [Iterative Closest Point (ICP) Matching](#iterative-closest-point-icp-matching)
|
||||
* [EKF SLAM](#ekf-slam)
|
||||
* [FastSLAM 1.0](#fastslam-10)
|
||||
* [FastSLAM 2.0](#fastslam-20)
|
||||
* [Graph based SLAM](#graph-based-slam)
|
||||
* [Path Planning](#path-planning)
|
||||
* [Dynamic Window Approach](#dynamic-window-approach)
|
||||
* [Grid based search](#grid-based-search)
|
||||
* [Dijkstra algorithm](#dijkstra-algorithm)
|
||||
* [A* algorithm](#a-algorithm)
|
||||
* [Potential Field algorithm](#potential-field-algorithm)
|
||||
* [Model Predictive Trajectory Generator](#model-predictive-trajectory-generator)
|
||||
* [Path optimization sample](#path-optimization-sample)
|
||||
* [Lookup table generation sample](#lookup-table-generation-sample)
|
||||
* [State Lattice Planning](#state-lattice-planning)
|
||||
* [Uniform polar sampling](#uniform-polar-sampling)
|
||||
* [Biased polar sampling](#biased-polar-sampling)
|
||||
* [Lane sampling](#lane-sampling)
|
||||
* [Probabilistic Road-Map (PRM) planning](#probabilistic-road-map-prm-planning)
|
||||
* [Voronoi Road-Map planning](#voronoi-road-map-planning)
|
||||
* [Rapidly-Exploring Random Trees (RRT)](#rapidly-exploring-random-trees-rrt)
|
||||
* [Basic RRT](#basic-rrt)
|
||||
* [RRT*](#rrt)
|
||||
* [RRT with dubins path](#rrt-with-dubins-path)
|
||||
* [RRT* with dubins path](#rrt-with-dubins-path-1)
|
||||
* [RRT* with reeds-sheep path](#rrt-with-reeds-sheep-path)
|
||||
* [Informed RRT*](#informed-rrt)
|
||||
* [Batch Informed RRT*](#batch-informed-rrt)
|
||||
* [Closed Loop RRT*](#closed-loop-rrt)
|
||||
* [LQR-RRT*](#lqr-rrt)
|
||||
* [Cubic spline planning](#cubic-spline-planning)
|
||||
* [B-Spline planning](#b-spline-planning)
|
||||
* [Eta^3 Spline path planning](#eta3-spline-path-planning)
|
||||
* [Bezier path planning](#bezier-path-planning)
|
||||
* [Quintic polynomials planning](#quintic-polynomials-planning)
|
||||
* [Dubins path planning](#dubins-path-planning)
|
||||
* [Reeds Shepp planning](#reeds-shepp-planning)
|
||||
* [LQR based path planning](#lqr-based-path-planning)
|
||||
* [Optimal Trajectory in a Frenet Frame](#optimal-trajectory-in-a-frenet-frame)
|
||||
* [Path tracking](#path-tracking)
|
||||
* [move to a pose control](#move-to-a-pose-control)
|
||||
* [Pure pursuit tracking](#pure-pursuit-tracking)
|
||||
* [Stanley control](#stanley-control)
|
||||
* [Rear wheel feedback control](#rear-wheel-feedback-control)
|
||||
* [Linear–quadratic regulator (LQR) steering control](#linearquadratic-regulator-lqr-steering-control)
|
||||
* [Linear–quadratic regulator (LQR) speed and steering control](#linearquadratic-regulator-lqr-speed-and-steering-control)
|
||||
* [Model predictive speed and steering control](#model-predictive-speed-and-steering-control)
|
||||
* [Arm Navigation](#arm-navigation)
|
||||
* [Two joint arm to point control](#two-joint-arm-to-point-control)
|
||||
* [N joint arm to point control](#n-joint-arm-to-point-control)
|
||||
* [Aerial Navigation](#aerial-navigation)
|
||||
* [drone 3d trajectory following](#drone-3d-trajectory-following)
|
||||
* [Documentation](#documentation)
|
||||
* [License](#license)
|
||||
* [Contribution](#contribution)
|
||||
* [Support](#support)
|
||||
@@ -94,20 +32,18 @@ See this paper for more details:
|
||||
|
||||
- [\[1808\.10703\] PythonRobotics: a Python code collection of robotics algorithms](https://arxiv.org/abs/1808.10703) ([BibTeX](https://github.com/AtsushiSakai/PythonRoboticsPaper/blob/master/python_robotics.bib))
|
||||
|
||||
# Documentation
|
||||
|
||||
Documentation is available online: [https://pythonrobotics.readthedocs.io/](https://pythonrobotics.readthedocs.io/)
|
||||
|
||||
# Requirements
|
||||
|
||||
- Python 3.6.x
|
||||
|
||||
- numpy
|
||||
|
||||
- scipy
|
||||
|
||||
- matplotlib
|
||||
|
||||
- pandas
|
||||
|
||||
- [cvxpy](http://www.cvxpy.org/en/latest/)
|
||||
- [cvxpy](http://www.cvxpy.org/en/latest/)
|
||||
|
||||
# How to use
|
||||
|
||||
@@ -117,661 +53,10 @@ See this paper for more details:
|
||||
|
||||
3. Execute python script in each directory.
|
||||
|
||||
4. Add star to this repo if you like it :smiley:.
|
||||
4. Add star to this repo if you like it :smiley:.
|
||||
|
||||
# Localization
|
||||
|
||||
## Extended Kalman Filter localization
|
||||
|
||||
<img src="https://github.com/AtsushiSakai/PythonRobotics/raw/master/Localization/extended_kalman_filter/animation.gif" width="640">
|
||||
|
||||
This is a sensor fusion localization with Extended Kalman Filter(EKF).
|
||||
|
||||
The blue line is true trajectory, the black line is dead reckoning trajectory,
|
||||
|
||||
the green point is positioning observation (ex. GPS), and the red line is estimated trajectory with EKF.
|
||||
|
||||
The red ellipse is estimated covariance ellipse with EKF.
|
||||
|
||||
Ref:
|
||||
|
||||
- [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/)
|
||||
|
||||
## Unscented Kalman Filter localization
|
||||
|
||||
|
||||
|
||||
This is a sensor fusion localization with Unscented Kalman Filter(UKF).
|
||||
|
||||
The lines and points are same meaning of the EKF simulation.
|
||||
|
||||
Ref:
|
||||
|
||||
- [Discriminatively Trained Unscented Kalman Filter for Mobile Robot Localization](https://www.researchgate.net/publication/267963417_Discriminatively_Trained_Unscented_Kalman_Filter_for_Mobile_Robot_Localization)
|
||||
|
||||
|
||||
## Particle filter localization
|
||||
|
||||
|
||||
|
||||
This is a sensor fusion localization with Particle Filter(PF).
|
||||
|
||||
The blue line is true trajectory, the black line is dead reckoning trajectory,
|
||||
|
||||
and the red line is estimated trajectory with PF.
|
||||
|
||||
It is assumed that the robot can measure a distance from landmarks (RFID).
|
||||
|
||||
This measurements are used for PF localization.
|
||||
|
||||
Ref:
|
||||
|
||||
- [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/)
|
||||
|
||||
|
||||
## Histogram filter localization
|
||||
|
||||
|
||||
|
||||
This is a 2D localization example with Histogram filter.
|
||||
|
||||
The red cross is true position, black points are RFID positions.
|
||||
|
||||
The blue grid shows a position probability of histogram filter.
|
||||
|
||||
In this simulation, x,y are unknown, yaw is known.
|
||||
|
||||
The filter integrates speed input and range observations from RFID for localization.
|
||||
|
||||
Initial position is not needed.
|
||||
|
||||
Ref:
|
||||
|
||||
- [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/)
|
||||
|
||||
# Mapping
|
||||
|
||||
## Gaussian grid map
|
||||
|
||||
This is a 2D Gaussian grid mapping example.
|
||||
|
||||
|
||||
|
||||
## Ray casting grid map
|
||||
|
||||
This is a 2D ray casting grid mapping example.
|
||||
|
||||
|
||||
|
||||
## k-means object clustering
|
||||
|
||||
This is a 2D object clustering with k-means algorithm.
|
||||
|
||||
|
||||
|
||||
## Object shape recognition using circle fitting
|
||||
|
||||
This is an object shape recognition using circle fitting.
|
||||
|
||||
|
||||
|
||||
The blue circle is the true object shape.
|
||||
|
||||
The red crosses are observations from a ranging sensor.
|
||||
|
||||
The red circle is the estimated object shape using circle fitting.
|
||||
|
||||
# SLAM
|
||||
|
||||
Simultaneous Localization and Mapping(SLAM) examples
|
||||
|
||||
## Iterative Closest Point (ICP) Matching
|
||||
|
||||
This is a 2D ICP matching example with singular value decomposition.
|
||||
|
||||
It can calculate a rotation matrix and a translation vector between points to points.
|
||||
|
||||
|
||||
|
||||
Ref:
|
||||
|
||||
- [Introduction to Mobile Robotics: Iterative Closest Point Algorithm](https://cs.gmu.edu/~kosecka/cs685/cs685-icp.pdf)
|
||||
|
||||
|
||||
## EKF SLAM
|
||||
|
||||
This is an Extended Kalman Filter based SLAM example.
|
||||
|
||||
The blue line is ground truth, the black line is dead reckoning, the red line is the estimated trajectory with EKF SLAM.
|
||||
|
||||
The green crosses are estimated landmarks.
|
||||
|
||||
|
||||
|
||||
Ref:
|
||||
|
||||
- [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/)
|
||||
|
||||
## FastSLAM 1.0
|
||||
|
||||
This is a feature based SLAM example using FastSLAM 1.0.
|
||||
|
||||
The blue line is ground truth, the black line is dead reckoning, the red line is the estimated trajectory with FastSLAM.
|
||||
|
||||
The red points are particles of FastSLAM.
|
||||
|
||||
Black points are landmarks, blue crosses are estimated landmark positions by FastSLAM.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Ref:
|
||||
|
||||
- [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/)
|
||||
|
||||
- [SLAM simulations by Tim Bailey](http://www-personal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm)
|
||||
|
||||
## FastSLAM 2.0
|
||||
|
||||
This is a feature based SLAM example using FastSLAM 2.0.
|
||||
|
||||
The animation has the same meanings as one of FastSLAM 1.0.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Ref:
|
||||
|
||||
- [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/)
|
||||
|
||||
- [SLAM simulations by Tim Bailey](http://www-personal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm)
|
||||
|
||||
|
||||
## Graph based SLAM
|
||||
|
||||
This is a graph based SLAM example.
|
||||
|
||||
The blue line is ground truth.
|
||||
|
||||
The black line is dead reckoning.
|
||||
|
||||
The red line is the estimated trajectory with Graph based SLAM.
|
||||
|
||||
The black stars are landmarks for graph edge generation.
|
||||
|
||||
|
||||
|
||||
Ref:
|
||||
|
||||
- [A Tutorial on Graph-Based SLAM](http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf)
|
||||
|
||||
|
||||
# Path Planning
|
||||
|
||||
## Dynamic Window Approach
|
||||
|
||||
This is a 2D navigation sample code with Dynamic Window Approach.
|
||||
|
||||
- [The Dynamic Window Approach to Collision Avoidance](https://www.ri.cmu.edu/pub_files/pub1/fox_dieter_1997_1/fox_dieter_1997_1.pdf)
|
||||
|
||||
|
||||
|
||||
|
||||
## Grid based search
|
||||
|
||||
### Dijkstra algorithm
|
||||
|
||||
This is a 2D grid based shortest path planning with Dijkstra's algorithm.
|
||||
|
||||
|
||||
|
||||
In the animation, cyan points are searched nodes.
|
||||
|
||||
### A\* algorithm
|
||||
|
||||
This is a 2D grid based shortest path planning with A star algorithm.
|
||||
|
||||
|
||||
|
||||
In the animation, cyan points are searched nodes.
|
||||
|
||||
Its heuristic is 2D Euclid distance.
|
||||
|
||||
### Potential Field algorithm
|
||||
|
||||
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:
|
||||
|
||||
- [Robotic Motion Planning:Potential Functions](https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf)
|
||||
|
||||
|
||||
## Model Predictive Trajectory Generator
|
||||
|
||||
This is a path optimization sample on model predictive trajectory generator.
|
||||
|
||||
This algorithm is used for state lattice planner.
|
||||
|
||||
### Path optimization sample
|
||||
|
||||
|
||||
|
||||
### Lookup table generation sample
|
||||
|
||||
|
||||
|
||||
Ref:
|
||||
|
||||
- [Optimal rough terrain trajectory generation for wheeled mobile robots](http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328)
|
||||
|
||||
|
||||
## State Lattice Planning
|
||||
|
||||
This script is a path planning code with state lattice planning.
|
||||
|
||||
This code uses the model predictive trajectory generator to solve boundary problem.
|
||||
|
||||
Ref:
|
||||
|
||||
- [Optimal rough terrain trajectory generation for wheeled mobile robots](http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328)
|
||||
|
||||
- [State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex Environments](http://www.frc.ri.cmu.edu/~alonzo/pubs/papers/JFR_08_SS_Sampling.pdf)
|
||||
|
||||
|
||||
### Uniform polar sampling
|
||||
|
||||
|
||||
|
||||
|
||||
### Biased polar sampling
|
||||
|
||||
|
||||
|
||||
|
||||
### Lane sampling
|
||||
|
||||
|
||||
|
||||
## Probabilistic Road-Map (PRM) planning
|
||||
|
||||
|
||||
|
||||
This PRM planner uses Dijkstra method for graph search.
|
||||
|
||||
In the animation, blue points are sampled points,
|
||||
|
||||
Cyan crosses means searched points with Dijkstra method,
|
||||
|
||||
The red line is the final path of PRM.
|
||||
|
||||
Ref:
|
||||
|
||||
- [Probabilistic roadmap \- Wikipedia](https://en.wikipedia.org/wiki/Probabilistic_roadmap)
|
||||
|
||||
|
||||
## Voronoi Road-Map planning
|
||||
|
||||
|
||||
|
||||
This Voronoi road-map planner uses Dijkstra method for graph search.
|
||||
|
||||
In the animation, blue points are Voronoi points,
|
||||
|
||||
Cyan crosses mean searched points with Dijkstra method,
|
||||
|
||||
The red line is the final path of Vornoi Road-Map.
|
||||
|
||||
Ref:
|
||||
|
||||
- [Robotic Motion Planning](https://www.cs.cmu.edu/~motionplanning/lecture/Chap5-RoadMap-Methods_howie.pdf)
|
||||
|
||||
|
||||
## Rapidly-Exploring Random Trees (RRT)
|
||||
|
||||
### Basic RRT
|
||||
|
||||
|
||||
|
||||
This is a simple path planning code with Rapidly-Exploring Random Trees (RRT)
|
||||
|
||||
Black circles are obstacles, green line is a searched tree, red crosses are start and goal positions.
|
||||
|
||||
### RRT\*
|
||||
|
||||
|
||||
|
||||
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:
|
||||
|
||||
- [Incremental Sampling-based Algorithms for Optimal Motion Planning](https://arxiv.org/abs/1005.0416)
|
||||
|
||||
- [Sampling-based Algorithms for Optimal Motion Planning](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.419.5503&rep=rep1&type=pdf)
|
||||
|
||||
|
||||
### RRT with dubins path
|
||||
|
||||
|
||||
|
||||
Path planning for a car robot with RRT and dubins path planner.
|
||||
|
||||
|
||||
### RRT\* with dubins path
|
||||
|
||||
|
||||
|
||||
Path planning for a car robot with RRT\* and dubins path planner.
|
||||
|
||||
|
||||
### RRT\* with reeds-sheep path
|
||||
|
||||
)
|
||||
|
||||
Path planning for a car robot with RRT\* and reeds sheep path planner.
|
||||
|
||||
### Informed RRT\*
|
||||
|
||||
)
|
||||
|
||||
This is a path planning code with Informed RRT\*.
|
||||
|
||||
The cyan ellipse is the heuristic sampling domain of Informed RRT\*.
|
||||
|
||||
Ref:
|
||||
|
||||
- [Informed RRT\*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic](https://arxiv.org/pdf/1404.2334.pdf)
|
||||
|
||||
### Batch Informed RRT\*
|
||||
|
||||
)
|
||||
|
||||
This is a path planning code with Batch Informed RRT\*.
|
||||
|
||||
Ref:
|
||||
|
||||
- [Batch Informed Trees \(BIT\*\): Sampling\-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs](https://arxiv.org/abs/1405.5848)
|
||||
|
||||
### Closed Loop RRT\*
|
||||
|
||||
A vehicle model based path planning with closed loop RRT\*.
|
||||
|
||||
|
||||
|
||||
In this code, pure-pursuit algorithm is used for steering control,
|
||||
|
||||
PID is used for speed control.
|
||||
|
||||
Ref:
|
||||
|
||||
- [Motion Planning in Complex Environments
|
||||
using Closed-loop Prediction](http://acl.mit.edu/papers/KuwataGNC08.pdf)
|
||||
|
||||
- [Real-time Motion Planning with Applications to
|
||||
Autonomous Urban Driving](http://acl.mit.edu/papers/KuwataTCST09.pdf)
|
||||
|
||||
- [[1601.06326] Sampling-based Algorithms for Optimal Motion Planning Using Closed-loop Prediction](https://arxiv.org/abs/1601.06326)
|
||||
|
||||
### LQR-RRT\*
|
||||
|
||||
This is a path planning simulation with LQR-RRT\*.
|
||||
|
||||
A double integrator motion model is used for LQR local planner.
|
||||
|
||||
|
||||
|
||||
Ref:
|
||||
|
||||
- [LQR\-RRT\*: Optimal Sampling\-Based Motion Planning with Automatically Derived Extension Heuristics](http://lis.csail.mit.edu/pubs/perez-icra12.pdf)
|
||||
|
||||
- [MahanFathi/LQR\-RRTstar: LQR\-RRT\* method is used for random motion planning of a simple pendulum in its phase plot](https://github.com/MahanFathi/LQR-RRTstar)
|
||||
|
||||
|
||||
## Cubic spline planning
|
||||
|
||||
A sample code for cubic path planning.
|
||||
|
||||
This code generates a curvature continuous path based on x-y waypoints with cubic spline.
|
||||
|
||||
Heading angle of each point can be also calculated analytically.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## B-Spline planning
|
||||
|
||||
|
||||
|
||||
This is a path planning with B-Spline curse.
|
||||
|
||||
If you input waypoints, it generates a smooth path with B-Spline curve.
|
||||
|
||||
The final course should be on the first and last waypoints.
|
||||
|
||||
Ref:
|
||||
|
||||
- [B\-spline \- Wikipedia](https://en.wikipedia.org/wiki/B-spline)
|
||||
|
||||
## Eta^3 Spline path planning
|
||||
|
||||
|
||||
|
||||
This is a path planning with Eta^3 spline.
|
||||
|
||||
Ref:
|
||||
|
||||
- [\eta^3-Splines for the Smooth Path Generation of Wheeled Mobile Robots](https://ieeexplore.ieee.org/document/4339545/)
|
||||
|
||||
|
||||
|
||||
## Bezier path planning
|
||||
|
||||
A sample code of Bezier path planning.
|
||||
|
||||
It is based on 4 control points Beier path.
|
||||
|
||||
|
||||
|
||||
If you change the offset distance from start and end point,
|
||||
|
||||
You can get different Beizer course:
|
||||
|
||||
|
||||
|
||||
|
||||
Ref:
|
||||
|
||||
- [Continuous Curvature Path Generation Based on Bezier Curves for Autonomous Vehicles](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.294.6438&rep=rep1&type=pdf)
|
||||
|
||||
## Quintic polynomials planning
|
||||
|
||||
Motion planning with quintic polynomials.
|
||||
|
||||
|
||||
|
||||
It can calculate 2D path, velocity, and acceleration profile based on quintic polynomials.
|
||||
|
||||
Ref:
|
||||
|
||||
- [Local Path Planning And Motion Control For Agv In Positioning](http://ieeexplore.ieee.org/document/637936/)
|
||||
|
||||
|
||||
## Dubins path planning
|
||||
|
||||
A sample code for Dubins path planning.
|
||||
|
||||
|
||||
|
||||
Ref:
|
||||
|
||||
- [Dubins path - Wikipedia](https://en.wikipedia.org/wiki/Dubins_path)
|
||||
|
||||
## Reeds Shepp planning
|
||||
|
||||
A sample code with Reeds Shepp path planning.
|
||||
|
||||
|
||||
|
||||
Ref:
|
||||
|
||||
- [15.3.2 Reeds\-Shepp Curves](http://planning.cs.uiuc.edu/node822.html)
|
||||
|
||||
- [optimal paths for a car that goes both forwards and backwards](https://pdfs.semanticscholar.org/932e/c495b1d0018fd59dee12a0bf74434fac7af4.pdf)
|
||||
|
||||
- [ghliu/pyReedsShepp: Implementation of Reeds Shepp curve\.](https://github.com/ghliu/pyReedsShepp)
|
||||
|
||||
|
||||
## LQR based path planning
|
||||
|
||||
A sample code using LQR based path planning for double integrator model.
|
||||
|
||||
|
||||
|
||||
|
||||
## Optimal Trajectory in a Frenet Frame
|
||||
|
||||
|
||||
|
||||
This is optimal trajectory generation in a Frenet Frame.
|
||||
|
||||
The cyan line is the target course and black crosses are obstacles.
|
||||
|
||||
The red line is predicted path.
|
||||
|
||||
Ref:
|
||||
|
||||
- [Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame](https://www.researchgate.net/profile/Moritz_Werling/publication/224156269_Optimal_Trajectory_Generation_for_Dynamic_Street_Scenarios_in_a_Frenet_Frame/links/54f749df0cf210398e9277af.pdf)
|
||||
|
||||
- [Optimal trajectory generation for dynamic street scenarios in a Frenet Frame](https://www.youtube.com/watch?v=Cj6tAQe7UCY)
|
||||
|
||||
|
||||
# Path tracking
|
||||
|
||||
## move to a pose control
|
||||
|
||||
This is a simulation of moving to a pose control
|
||||
|
||||
|
||||
|
||||
Ref:
|
||||
|
||||
- [P. I. Corke, "Robotics, Vision and Control" \| SpringerLink p102](https://link.springer.com/book/10.1007/978-3-642-20144-8)
|
||||
|
||||
|
||||
## Pure pursuit tracking
|
||||
|
||||
Path tracking simulation with pure pursuit steering control and PID 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.
|
||||
|
||||
Ref:
|
||||
|
||||
- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://arxiv.org/abs/1604.07446)
|
||||
|
||||
## Stanley control
|
||||
|
||||
Path tracking simulation with Stanley steering control and PID speed control.
|
||||
|
||||
|
||||
|
||||
Ref:
|
||||
|
||||
- [Stanley: The robot that won the DARPA grand challenge](http://robots.stanford.edu/papers/thrun.stanley05.pdf)
|
||||
|
||||
- [Automatic Steering Methods for Autonomous Automobile Path Tracking](https://www.ri.cmu.edu/pub_files/2009/2/Automatic_Steering_Methods_for_Autonomous_Automobile_Path_Tracking.pdf)
|
||||
|
||||
|
||||
|
||||
## Rear wheel feedback control
|
||||
|
||||
Path tracking simulation with rear wheel feedback steering control and PID speed control.
|
||||
|
||||
|
||||
|
||||
Ref:
|
||||
|
||||
- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://arxiv.org/abs/1604.07446)
|
||||
|
||||
|
||||
## Linear–quadratic regulator (LQR) steering control
|
||||
|
||||
Path tracking simulation with LQR steering control and PID speed control.
|
||||
|
||||
|
||||
|
||||
Ref:
|
||||
|
||||
- [ApolloAuto/apollo: An open autonomous driving platform](https://github.com/ApolloAuto/apollo)
|
||||
|
||||
|
||||
## Linear–quadratic regulator (LQR) speed and steering control
|
||||
|
||||
Path tracking simulation with LQR speed and steering control.
|
||||
|
||||
|
||||
|
||||
Ref:
|
||||
|
||||
- [Towards fully autonomous driving: Systems and algorithms \- IEEE Conference Publication](http://ieeexplore.ieee.org/document/5940562/)
|
||||
|
||||
|
||||
## Model predictive speed and steering control
|
||||
|
||||
Path tracking simulation with iterative linear model predictive speed and steering control.
|
||||
|
||||
<img src="https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathTracking/model_predictive_speed_and_steer_control/animation.gif" width="640">
|
||||
|
||||
Ref:
|
||||
|
||||
- [notebook](https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/model_predictive_speed_and_steer_control/notebook.ipynb)
|
||||
|
||||
|
||||
# Arm Navigation
|
||||
|
||||
## Two joint arm to point control
|
||||
|
||||
Two joint arm to a point control simulation.
|
||||
|
||||
This is a interactive simulation.
|
||||
|
||||
You can set the goal position of the end effector with left-click on the ploting area.
|
||||
|
||||
|
||||
|
||||
|
||||
## N joint arm to point control
|
||||
|
||||
N joint arm to a point control simulation.
|
||||
|
||||
This is a interactive simulation.
|
||||
|
||||
You can set the goal position of the end effector with left-click on the ploting area.
|
||||
|
||||
|
||||
|
||||
In this simulation N = 10, however, you can change it.
|
||||
|
||||
# Aerial Navigation
|
||||
|
||||
## drone 3d trajectory following
|
||||
|
||||
This is a 3d trajectory following simulation for a quadrotor.
|
||||
|
||||
|
||||
|
||||
|
||||
# License
|
||||
# License
|
||||
|
||||
MIT
|
||||
|
||||
@@ -795,7 +80,7 @@ E-mail consultant is also available.
|
||||
|
||||
|
||||
|
||||
Your comment using [](https://saythanks.io/to/AtsushiSakai) is also welcome.
|
||||
Your comment using [](https://saythanks.io/to/AtsushiSakai) is also welcome.
|
||||
|
||||
This is a list: [Users comments](https://github.com/AtsushiSakai/PythonRobotics/blob/master/users_comments.md)
|
||||
|
||||
@@ -812,6 +97,3 @@ This is a list: [Users comments](https://github.com/AtsushiSakai/PythonRobotics/
|
||||
- [Antonin RAFFIN](https://github.com/araffin)
|
||||
|
||||
- [Alexis Paques](https://github.com/AlexisTM)
|
||||
|
||||
|
||||
|
||||
|
||||
20
docs/Makefile
Normal file
20
docs/Makefile
Normal file
@@ -0,0 +1,20 @@
|
||||
# Minimal makefile for Sphinx documentation
|
||||
#
|
||||
|
||||
# You can set these variables from the command line.
|
||||
SPHINXOPTS =
|
||||
SPHINXBUILD = sphinx-build
|
||||
SPHINXPROJ = PythonRobotics
|
||||
SOURCEDIR = .
|
||||
BUILDDIR = _build
|
||||
|
||||
# Put it first so that "make" without argument is like "make help".
|
||||
help:
|
||||
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
|
||||
|
||||
.PHONY: help Makefile
|
||||
|
||||
# Catch-all target: route all unknown targets to Sphinx using the new
|
||||
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
|
||||
%: Makefile
|
||||
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
|
||||
25
docs/README.md
Normal file
25
docs/README.md
Normal file
@@ -0,0 +1,25 @@
|
||||
## Python Robotics Documentation
|
||||
|
||||
This folder contains documentation for the Python Robotics project.
|
||||
|
||||
|
||||
### Build the Documentation
|
||||
|
||||
#### Install Sphinx and Theme
|
||||
|
||||
```
|
||||
pip install sphinx sphinx-autobuild sphinx-rtd-theme
|
||||
```
|
||||
|
||||
#### Building the Docs
|
||||
|
||||
In the `docs/` folder:
|
||||
```
|
||||
make html
|
||||
```
|
||||
|
||||
if you want to building each time a file is changed:
|
||||
|
||||
```
|
||||
sphinx-autobuild . _build/html
|
||||
```
|
||||
169
docs/conf.py
Normal file
169
docs/conf.py
Normal file
@@ -0,0 +1,169 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
#
|
||||
# Configuration file for the Sphinx documentation builder.
|
||||
#
|
||||
# This file does only contain a selection of the most common options. For a
|
||||
# full list see the documentation:
|
||||
# http://www.sphinx-doc.org/en/master/config
|
||||
|
||||
# -- Path setup --------------------------------------------------------------
|
||||
|
||||
# If extensions (or modules to document with autodoc) are in another directory,
|
||||
# add these directories to sys.path here. If the directory is relative to the
|
||||
# documentation root, use os.path.abspath to make it absolute, like shown here.
|
||||
#
|
||||
import os
|
||||
# import sys
|
||||
# sys.path.insert(0, os.path.abspath('.'))
|
||||
|
||||
|
||||
# -- Project information -----------------------------------------------------
|
||||
|
||||
project = 'PythonRobotics'
|
||||
copyright = '2018, Atsushi Sakai'
|
||||
author = 'Atsushi Sakai'
|
||||
|
||||
# The short X.Y version
|
||||
version = ''
|
||||
# The full version, including alpha/beta/rc tags
|
||||
release = ''
|
||||
|
||||
|
||||
# -- General configuration ---------------------------------------------------
|
||||
|
||||
# If your documentation needs a minimal Sphinx version, state it here.
|
||||
#
|
||||
# needs_sphinx = '1.0'
|
||||
|
||||
# Add any Sphinx extension module names here, as strings. They can be
|
||||
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
|
||||
# ones.
|
||||
extensions = [
|
||||
'sphinx.ext.autodoc',
|
||||
'sphinx.ext.mathjax',
|
||||
'sphinx.ext.viewcode',
|
||||
]
|
||||
|
||||
# Add any paths that contain templates here, relative to this directory.
|
||||
templates_path = ['_templates']
|
||||
|
||||
# The suffix(es) of source filenames.
|
||||
# You can specify multiple suffix as a list of string:
|
||||
#
|
||||
# source_suffix = ['.rst', '.md']
|
||||
source_suffix = '.rst'
|
||||
|
||||
# The master toctree document.
|
||||
master_doc = 'index'
|
||||
|
||||
# The language for content autogenerated by Sphinx. Refer to documentation
|
||||
# for a list of supported languages.
|
||||
#
|
||||
# This is also used if you do content translation via gettext catalogs.
|
||||
# Usually you set "language" from the command line for these cases.
|
||||
language = None
|
||||
|
||||
# List of patterns, relative to source directory, that match files and
|
||||
# directories to ignore when looking for source files.
|
||||
# This pattern also affects html_static_path and html_extra_path .
|
||||
exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
|
||||
|
||||
# The name of the Pygments (syntax highlighting) style to use.
|
||||
pygments_style = 'sphinx'
|
||||
|
||||
|
||||
# -- Options for HTML output -------------------------------------------------
|
||||
|
||||
# The theme to use for HTML and HTML Help pages. See the documentation for
|
||||
# a list of builtin themes.
|
||||
#
|
||||
# Fix for read the docs
|
||||
on_rtd = os.environ.get('READTHEDOCS') == 'True'
|
||||
if on_rtd:
|
||||
html_theme = 'default'
|
||||
else:
|
||||
html_theme = 'sphinx_rtd_theme'
|
||||
|
||||
# Theme options are theme-specific and customize the look and feel of a theme
|
||||
# further. For a list of options available for each theme, see the
|
||||
# documentation.
|
||||
#
|
||||
html_logo = '../icon.png'
|
||||
html_theme_options = {
|
||||
'display_version': False,
|
||||
}
|
||||
|
||||
# Add any paths that contain custom static files (such as style sheets) here,
|
||||
# relative to this directory. They are copied after the builtin static files,
|
||||
# so a file named "default.css" will overwrite the builtin "default.css".
|
||||
html_static_path = ['_static']
|
||||
|
||||
# Custom sidebar templates, must be a dictionary that maps document names
|
||||
# to template names.
|
||||
#
|
||||
# The default sidebars (for documents that don't match any pattern) are
|
||||
# defined by theme itself. Builtin themes are using these templates by
|
||||
# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
|
||||
# 'searchbox.html']``.
|
||||
#
|
||||
# html_sidebars = {}
|
||||
|
||||
|
||||
# -- Options for HTMLHelp output ---------------------------------------------
|
||||
|
||||
# Output file base name for HTML help builder.
|
||||
htmlhelp_basename = 'PythonRoboticsdoc'
|
||||
|
||||
|
||||
# -- Options for LaTeX output ------------------------------------------------
|
||||
|
||||
latex_elements = {
|
||||
# The paper size ('letterpaper' or 'a4paper').
|
||||
#
|
||||
# 'papersize': 'letterpaper',
|
||||
|
||||
# The font size ('10pt', '11pt' or '12pt').
|
||||
#
|
||||
# 'pointsize': '10pt',
|
||||
|
||||
# Additional stuff for the LaTeX preamble.
|
||||
#
|
||||
# 'preamble': '',
|
||||
|
||||
# Latex figure (float) alignment
|
||||
#
|
||||
# 'figure_align': 'htbp',
|
||||
}
|
||||
|
||||
# Grouping the document tree into LaTeX files. List of tuples
|
||||
# (source start file, target name, title,
|
||||
# author, documentclass [howto, manual, or own class]).
|
||||
latex_documents = [
|
||||
(master_doc, 'PythonRobotics.tex', 'PythonRobotics Documentation',
|
||||
'Atsushi Sakai', 'manual'),
|
||||
]
|
||||
|
||||
|
||||
# -- Options for manual page output ------------------------------------------
|
||||
|
||||
# One entry per manual page. List of tuples
|
||||
# (source start file, name, description, authors, manual section).
|
||||
man_pages = [
|
||||
(master_doc, 'pythonrobotics', 'PythonRobotics Documentation',
|
||||
[author], 1)
|
||||
]
|
||||
|
||||
|
||||
# -- Options for Texinfo output ----------------------------------------------
|
||||
|
||||
# Grouping the document tree into Texinfo files. List of tuples
|
||||
# (source start file, target name, title, author,
|
||||
# dir menu entry, description, category)
|
||||
texinfo_documents = [
|
||||
(master_doc, 'PythonRobotics', 'PythonRobotics Documentation',
|
||||
author, 'PythonRobotics', 'One line description of project.',
|
||||
'Miscellaneous'),
|
||||
]
|
||||
|
||||
|
||||
# -- Extension configuration -------------------------------------------------
|
||||
29
docs/getting_started.rst
Normal file
29
docs/getting_started.rst
Normal file
@@ -0,0 +1,29 @@
|
||||
.. _getting_started:
|
||||
|
||||
Getting Started
|
||||
===============
|
||||
|
||||
Requirements
|
||||
-------------
|
||||
|
||||
- Python 3.6.x
|
||||
- numpy
|
||||
- scipy
|
||||
- matplotlib
|
||||
- pandas
|
||||
- `cvxpy`_
|
||||
|
||||
.. _cvxpy: http://www.cvxpy.org/en/latest/
|
||||
|
||||
|
||||
How to use
|
||||
----------
|
||||
|
||||
1. Install the required libraries. You can use environment.yml with
|
||||
conda command.
|
||||
|
||||
2. Clone this repo.
|
||||
|
||||
3. Execute python script in each directory.
|
||||
|
||||
4. Add star to this repo if you like it 😃.
|
||||
50
docs/index.rst
Normal file
50
docs/index.rst
Normal file
@@ -0,0 +1,50 @@
|
||||
.. PythonRobotics documentation master file, created by
|
||||
sphinx-quickstart on Sat Sep 15 13:15:55 2018.
|
||||
You can adapt this file completely to your liking, but it should at least
|
||||
contain the root `toctree` directive.
|
||||
|
||||
Welcome to PythonRobotics's documentation!
|
||||
==========================================
|
||||
|
||||
Python codes for robotics algorithm.
|
||||
|
||||
This is a Python code collection of robotics algorithms, especially for autonomous navigation.
|
||||
|
||||
Features:
|
||||
|
||||
1. Easy to read for understanding each algorithm's basic idea.
|
||||
|
||||
2. Widely used and practical algorithms are selected.
|
||||
|
||||
3. Minimum dependency.
|
||||
|
||||
See this paper for more details:
|
||||
|
||||
- `[1808.10703] PythonRobotics: a Python code collection of robotics
|
||||
algorithms`_ (`BibTeX`_)
|
||||
|
||||
.. _`[1808.10703] PythonRobotics: a Python code collection of robotics algorithms`: https://arxiv.org/abs/1808.10703
|
||||
.. _BibTeX: https://github.com/AtsushiSakai/PythonRoboticsPaper/blob/master/python_robotics.bib
|
||||
|
||||
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Guide
|
||||
|
||||
getting_started
|
||||
modules/localization
|
||||
modules/mapping
|
||||
modules/slam
|
||||
modules/path_planning
|
||||
modules/path_tracking
|
||||
modules/arm_navigation
|
||||
modules/aerial_navigation
|
||||
|
||||
|
||||
Indices and tables
|
||||
==================
|
||||
|
||||
* :ref:`genindex`
|
||||
* :ref:`modindex`
|
||||
* :ref:`search`
|
||||
36
docs/make.bat
Normal file
36
docs/make.bat
Normal file
@@ -0,0 +1,36 @@
|
||||
@ECHO OFF
|
||||
|
||||
pushd %~dp0
|
||||
|
||||
REM Command file for Sphinx documentation
|
||||
|
||||
if "%SPHINXBUILD%" == "" (
|
||||
set SPHINXBUILD=sphinx-build
|
||||
)
|
||||
set SOURCEDIR=.
|
||||
set BUILDDIR=_build
|
||||
set SPHINXPROJ=PythonRobotics
|
||||
|
||||
if "%1" == "" goto help
|
||||
|
||||
%SPHINXBUILD% >NUL 2>NUL
|
||||
if errorlevel 9009 (
|
||||
echo.
|
||||
echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
|
||||
echo.installed, then set the SPHINXBUILD environment variable to point
|
||||
echo.to the full path of the 'sphinx-build' executable. Alternatively you
|
||||
echo.may add the Sphinx directory to PATH.
|
||||
echo.
|
||||
echo.If you don't have Sphinx installed, grab it from
|
||||
echo.http://sphinx-doc.org/
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
|
||||
goto end
|
||||
|
||||
:help
|
||||
%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
|
||||
|
||||
:end
|
||||
popd
|
||||
13
docs/modules/aerial_navigation.rst
Normal file
13
docs/modules/aerial_navigation.rst
Normal file
@@ -0,0 +1,13 @@
|
||||
.. _aerial_navigation:
|
||||
|
||||
Aerial Navigation
|
||||
=================
|
||||
|
||||
Drone 3d trajectory following
|
||||
-----------------------------
|
||||
|
||||
This is a 3d trajectory following simulation for a quadrotor.
|
||||
|
||||
|3|
|
||||
|
||||
.. |3| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/AerialNavigation/drone_3d_trajectory_following/animation.gif
|
||||
33
docs/modules/arm_navigation.rst
Normal file
33
docs/modules/arm_navigation.rst
Normal file
@@ -0,0 +1,33 @@
|
||||
.. _arm_navigation:
|
||||
|
||||
Arm Navigation
|
||||
==============
|
||||
|
||||
Two joint arm to point control
|
||||
------------------------------
|
||||
|
||||
Two joint arm to a point control simulation.
|
||||
|
||||
This is a interactive simulation.
|
||||
|
||||
You can set the goal position of the end effector with left-click on the
|
||||
ploting area.
|
||||
|
||||
|3|
|
||||
|
||||
N joint arm to point control
|
||||
----------------------------
|
||||
|
||||
N joint arm to a point control simulation.
|
||||
|
||||
This is a interactive simulation.
|
||||
|
||||
You can set the goal position of the end effector with left-click on the
|
||||
ploting area.
|
||||
|
||||
|4|
|
||||
|
||||
In this simulation N = 10, however, you can change it.
|
||||
|
||||
.. |3| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/ArmNavigation/two_joint_arm_to_point_control/animation.gif
|
||||
.. |4| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/ArmNavigation/n_joint_arm_to_point_control/animation.gif
|
||||
89
docs/modules/localization.rst
Normal file
89
docs/modules/localization.rst
Normal file
@@ -0,0 +1,89 @@
|
||||
.. _localization:
|
||||
|
||||
Localization
|
||||
============
|
||||
|
||||
Extended Kalman Filter localization
|
||||
-----------------------------------
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<img src="https://github.com/AtsushiSakai/PythonRobotics/raw/master/Localization/extended_kalman_filter/animation.gif" width="640">
|
||||
|
||||
This is a sensor fusion localization with Extended Kalman Filter(EKF).
|
||||
|
||||
The blue line is true trajectory, the black line is dead reckoning
|
||||
trajectory,
|
||||
|
||||
the green point is positioning observation (ex. GPS), and the red line
|
||||
is estimated trajectory with EKF.
|
||||
|
||||
The red ellipse is estimated covariance ellipse with EKF.
|
||||
|
||||
Ref:
|
||||
|
||||
- `PROBABILISTIC ROBOTICS`_
|
||||
|
||||
Unscented Kalman Filter localization
|
||||
------------------------------------
|
||||
|
||||
|2|
|
||||
|
||||
This is a sensor fusion localization with Unscented Kalman Filter(UKF).
|
||||
|
||||
The lines and points are same meaning of the EKF simulation.
|
||||
|
||||
Ref:
|
||||
|
||||
- `Discriminatively Trained Unscented Kalman Filter for Mobile Robot
|
||||
Localization`_
|
||||
|
||||
Particle filter localization
|
||||
----------------------------
|
||||
|
||||
|3|
|
||||
|
||||
This is a sensor fusion localization with Particle Filter(PF).
|
||||
|
||||
The blue line is true trajectory, the black line is dead reckoning
|
||||
trajectory,
|
||||
|
||||
and the red line is estimated trajectory with PF.
|
||||
|
||||
It is assumed that the robot can measure a distance from landmarks
|
||||
(RFID).
|
||||
|
||||
This measurements are used for PF localization.
|
||||
|
||||
Ref:
|
||||
|
||||
- `PROBABILISTIC ROBOTICS`_
|
||||
|
||||
Histogram filter localization
|
||||
-----------------------------
|
||||
|
||||
|4|
|
||||
|
||||
This is a 2D localization example with Histogram filter.
|
||||
|
||||
The red cross is true position, black points are RFID positions.
|
||||
|
||||
The blue grid shows a position probability of histogram filter.
|
||||
|
||||
In this simulation, x,y are unknown, yaw is known.
|
||||
|
||||
The filter integrates speed input and range observations from RFID for
|
||||
localization.
|
||||
|
||||
Initial position is not needed.
|
||||
|
||||
Ref:
|
||||
|
||||
- `PROBABILISTIC ROBOTICS`_
|
||||
|
||||
.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/
|
||||
.. _Discriminatively Trained Unscented Kalman Filter for Mobile Robot Localization: https://www.researchgate.net/publication/267963417_Discriminatively_Trained_Unscented_Kalman_Filter_for_Mobile_Robot_Localization
|
||||
|
||||
.. |2| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/Localization/unscented_kalman_filter/animation.gif
|
||||
.. |3| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/Localization/particle_filter/animation.gif
|
||||
.. |4| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/Localization/histogram_filter/animation.gif
|
||||
43
docs/modules/mapping.rst
Normal file
43
docs/modules/mapping.rst
Normal file
@@ -0,0 +1,43 @@
|
||||
.. _mapping:
|
||||
|
||||
Mapping
|
||||
=======
|
||||
|
||||
Gaussian grid map
|
||||
-----------------
|
||||
|
||||
This is a 2D Gaussian grid mapping example.
|
||||
|
||||
|2|
|
||||
|
||||
Ray casting grid map
|
||||
--------------------
|
||||
|
||||
This is a 2D ray casting grid mapping example.
|
||||
|
||||
|3|
|
||||
|
||||
k-means object clustering
|
||||
-------------------------
|
||||
|
||||
This is a 2D object clustering with k-means algorithm.
|
||||
|
||||
|4|
|
||||
|
||||
Object shape recognition using circle fitting
|
||||
---------------------------------------------
|
||||
|
||||
This is an object shape recognition using circle fitting.
|
||||
|
||||
|5|
|
||||
|
||||
The blue circle is the true object shape.
|
||||
|
||||
The red crosses are observations from a ranging sensor.
|
||||
|
||||
The red circle is the estimated object shape using circle fitting.
|
||||
|
||||
.. |2| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/Mapping/gaussian_grid_map/animation.gif
|
||||
.. |3| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/Mapping/raycasting_grid_map/animation.gif
|
||||
.. |4| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/Mapping/kmeans_clustering/animation.gif
|
||||
.. |5| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/Mapping/circle_fitting/animation.gif
|
||||
456
docs/modules/path_planning.rst
Normal file
456
docs/modules/path_planning.rst
Normal file
@@ -0,0 +1,456 @@
|
||||
.. _path_planning:
|
||||
|
||||
Path Planning
|
||||
=============
|
||||
|
||||
Dynamic Window Approach
|
||||
-----------------------
|
||||
|
||||
This is a 2D navigation sample code with Dynamic Window Approach.
|
||||
|
||||
- `The Dynamic Window Approach to Collision
|
||||
Avoidance <https://www.ri.cmu.edu/pub_files/pub1/fox_dieter_1997_1/fox_dieter_1997_1.pdf>`__
|
||||
|
||||
|DWA|
|
||||
|
||||
Grid based search
|
||||
-----------------
|
||||
|
||||
Dijkstra algorithm
|
||||
~~~~~~~~~~~~~~~~~~
|
||||
|
||||
This is a 2D grid based shortest path planning with Dijkstra's
|
||||
algorithm.
|
||||
|
||||
|Dijkstra|
|
||||
|
||||
In the animation, cyan points are searched nodes.
|
||||
|
||||
.. _a*-algorithm:
|
||||
|
||||
A\* algorithm
|
||||
~~~~~~~~~~~~~
|
||||
|
||||
This is a 2D grid based shortest path planning with A star algorithm.
|
||||
|
||||
|astar|
|
||||
|
||||
In the animation, cyan points are searched nodes.
|
||||
|
||||
Its heuristic is 2D Euclid distance.
|
||||
|
||||
Potential Field algorithm
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
This is a 2D grid based path planning with Potential Field algorithm.
|
||||
|
||||
|PotentialField|
|
||||
|
||||
In the animation, the blue heat map shows potential value on each grid.
|
||||
|
||||
Ref:
|
||||
|
||||
- `Robotic Motion Planning:Potential
|
||||
Functions <https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf>`__
|
||||
|
||||
Model Predictive Trajectory Generator
|
||||
-------------------------------------
|
||||
|
||||
This is a path optimization sample on model predictive trajectory
|
||||
generator.
|
||||
|
||||
This algorithm is used for state lattice planner.
|
||||
|
||||
Path optimization sample
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|4|
|
||||
|
||||
Lookup table generation sample
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|5|
|
||||
|
||||
Ref:
|
||||
|
||||
- `Optimal rough terrain trajectory generation for wheeled mobile
|
||||
robots <http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
|
||||
|
||||
State Lattice Planning
|
||||
----------------------
|
||||
|
||||
This script is a path planning code with state lattice planning.
|
||||
|
||||
This code uses the model predictive trajectory generator to solve
|
||||
boundary problem.
|
||||
|
||||
Ref:
|
||||
|
||||
- `Optimal rough terrain trajectory generation for wheeled mobile
|
||||
robots <http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
|
||||
|
||||
- `State Space Sampling of Feasible Motions for High-Performance Mobile
|
||||
Robot Navigation in Complex
|
||||
Environments <http://www.frc.ri.cmu.edu/~alonzo/pubs/papers/JFR_08_SS_Sampling.pdf>`__
|
||||
|
||||
Uniform polar sampling
|
||||
~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|6|
|
||||
|
||||
Biased polar sampling
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|7|
|
||||
|
||||
Lane sampling
|
||||
~~~~~~~~~~~~~
|
||||
|
||||
|8|
|
||||
|
||||
.. _probabilistic-road-map-(prm)-planning:
|
||||
|
||||
Probabilistic Road-Map (PRM) planning
|
||||
-------------------------------------
|
||||
|
||||
|PRM|
|
||||
|
||||
This PRM planner uses Dijkstra method for graph search.
|
||||
|
||||
In the animation, blue points are sampled points,
|
||||
|
||||
Cyan crosses means searched points with Dijkstra method,
|
||||
|
||||
The red line is the final path of PRM.
|
||||
|
||||
Ref:
|
||||
|
||||
- `Probabilistic roadmap -
|
||||
Wikipedia <https://en.wikipedia.org/wiki/Probabilistic_roadmap>`__
|
||||
|
||||
|
||||
|
||||
Voronoi Road-Map planning
|
||||
-------------------------
|
||||
|
||||
|VRM|
|
||||
|
||||
This Voronoi road-map planner uses Dijkstra method for graph search.
|
||||
|
||||
In the animation, blue points are Voronoi points,
|
||||
|
||||
Cyan crosses mean searched points with Dijkstra method,
|
||||
|
||||
The red line is the final path of Vornoi Road-Map.
|
||||
|
||||
Ref:
|
||||
|
||||
- `Robotic Motion
|
||||
Planning <https://www.cs.cmu.edu/~motionplanning/lecture/Chap5-RoadMap-Methods_howie.pdf>`__
|
||||
|
||||
.. _rapidly-exploring-random-trees-(rrt):
|
||||
|
||||
Rapidly-Exploring Random Trees (RRT)
|
||||
------------------------------------
|
||||
|
||||
Basic RRT
|
||||
~~~~~~~~~
|
||||
|
||||
|9|
|
||||
|
||||
This is a simple path planning code with Rapidly-Exploring Random Trees
|
||||
(RRT)
|
||||
|
||||
Black circles are obstacles, green line is a searched tree, red crosses
|
||||
are start and goal positions.
|
||||
|
||||
.. _rrt*:
|
||||
|
||||
RRT\*
|
||||
~~~~~
|
||||
|
||||
|10|
|
||||
|
||||
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:
|
||||
|
||||
- `Incremental Sampling-based Algorithms for Optimal Motion
|
||||
Planning <https://arxiv.org/abs/1005.0416>`__
|
||||
|
||||
- `Sampling-based Algorithms for Optimal Motion
|
||||
Planning <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.419.5503&rep=rep1&type=pdf>`__
|
||||
|
||||
RRT with dubins path
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|PythonRobotics|
|
||||
|
||||
Path planning for a car robot with RRT and dubins path planner.
|
||||
|
||||
.. _rrt*-with-dubins-path:
|
||||
|
||||
RRT\* with dubins path
|
||||
~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|AtsushiSakai/PythonRobotics|
|
||||
|
||||
Path planning for a car robot with RRT\* and dubins path planner.
|
||||
|
||||
.. _rrt*-with-reeds-sheep-path:
|
||||
|
||||
RRT\* with reeds-sheep path
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|11|
|
||||
|
||||
Path planning for a car robot with RRT\* and reeds sheep path planner.
|
||||
|
||||
.. _informed-rrt*:
|
||||
|
||||
Informed RRT\*
|
||||
~~~~~~~~~~~~~~
|
||||
|
||||
|irrt|
|
||||
|
||||
This is a path planning code with Informed RRT*.
|
||||
|
||||
The cyan ellipse is the heuristic sampling domain of Informed RRT*.
|
||||
|
||||
Ref:
|
||||
|
||||
- `Informed RRT\*: Optimal Sampling-based Path Planning Focused via
|
||||
Direct Sampling of an Admissible Ellipsoidal
|
||||
Heuristic <https://arxiv.org/pdf/1404.2334.pdf>`__
|
||||
|
||||
.. _batch-informed-rrt*:
|
||||
|
||||
Batch Informed RRT\*
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|irrt2|
|
||||
|
||||
This is a path planning code with Batch Informed RRT*.
|
||||
|
||||
Ref:
|
||||
|
||||
- `Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the
|
||||
Heuristically Guided Search of Implicit Random Geometric
|
||||
Graphs <https://arxiv.org/abs/1405.5848>`__
|
||||
|
||||
.. _closed-loop-rrt*:
|
||||
|
||||
Closed Loop RRT\*
|
||||
~~~~~~~~~~~~~~~~~
|
||||
|
||||
A vehicle model based path planning with closed loop RRT*.
|
||||
|
||||
|CLRRT|
|
||||
|
||||
In this code, pure-pursuit algorithm is used for steering control,
|
||||
|
||||
PID is used for speed control.
|
||||
|
||||
Ref:
|
||||
|
||||
- `Motion Planning in Complex Environments using Closed-loop
|
||||
Prediction <http://acl.mit.edu/papers/KuwataGNC08.pdf>`__
|
||||
|
||||
- `Real-time Motion Planning with Applications to Autonomous Urban
|
||||
Driving <http://acl.mit.edu/papers/KuwataTCST09.pdf>`__
|
||||
|
||||
- `[1601.06326] Sampling-based Algorithms for Optimal Motion Planning
|
||||
Using Closed-loop Prediction <https://arxiv.org/abs/1601.06326>`__
|
||||
|
||||
.. _lqr-rrt*:
|
||||
|
||||
LQR-RRT\*
|
||||
~~~~~~~~~
|
||||
|
||||
This is a path planning simulation with LQR-RRT*.
|
||||
|
||||
A double integrator motion model is used for LQR local planner.
|
||||
|
||||
|LQRRRT|
|
||||
|
||||
Ref:
|
||||
|
||||
- `LQR-RRT\*: Optimal Sampling-Based Motion Planning with Automatically
|
||||
Derived Extension
|
||||
Heuristics <http://lis.csail.mit.edu/pubs/perez-icra12.pdf>`__
|
||||
|
||||
- `MahanFathi/LQR-RRTstar: LQR-RRT\* method is used for random motion
|
||||
planning of a simple pendulum in its phase
|
||||
plot <https://github.com/MahanFathi/LQR-RRTstar>`__
|
||||
|
||||
Cubic spline planning
|
||||
---------------------
|
||||
|
||||
A sample code for cubic path planning.
|
||||
|
||||
This code generates a curvature continuous path based on x-y waypoints
|
||||
with cubic spline.
|
||||
|
||||
Heading angle of each point can be also calculated analytically.
|
||||
|
||||
|12|
|
||||
|13|
|
||||
|14|
|
||||
|
||||
B-Spline planning
|
||||
-----------------
|
||||
|
||||
|B-Spline|
|
||||
|
||||
This is a path planning with B-Spline curse.
|
||||
|
||||
If you input waypoints, it generates a smooth path with B-Spline curve.
|
||||
|
||||
The final course should be on the first and last waypoints.
|
||||
|
||||
Ref:
|
||||
|
||||
- `B-spline - Wikipedia <https://en.wikipedia.org/wiki/B-spline>`__
|
||||
|
||||
.. _eta^3-spline-path-planning:
|
||||
|
||||
Eta^3 Spline path planning
|
||||
--------------------------
|
||||
|
||||
|eta3|
|
||||
|
||||
This is a path planning with Eta^3 spline.
|
||||
|
||||
Ref:
|
||||
|
||||
- `\\eta^3-Splines for the Smooth Path Generation of Wheeled Mobile
|
||||
Robots <https://ieeexplore.ieee.org/document/4339545/>`__
|
||||
|
||||
Bezier path planning
|
||||
--------------------
|
||||
|
||||
A sample code of Bezier path planning.
|
||||
|
||||
It is based on 4 control points Beier path.
|
||||
|
||||
|Bezier1|
|
||||
|
||||
If you change the offset distance from start and end point,
|
||||
|
||||
You can get different Beizer course:
|
||||
|
||||
|Bezier2|
|
||||
|
||||
Ref:
|
||||
|
||||
- `Continuous Curvature Path Generation Based on Bezier Curves for
|
||||
Autonomous
|
||||
Vehicles <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.294.6438&rep=rep1&type=pdf>`__
|
||||
|
||||
Quintic polynomials planning
|
||||
----------------------------
|
||||
|
||||
Motion planning with quintic polynomials.
|
||||
|
||||
|2|
|
||||
|
||||
It can calculate 2D path, velocity, and acceleration profile based on
|
||||
quintic polynomials.
|
||||
|
||||
Ref:
|
||||
|
||||
- `Local Path Planning And Motion Control For Agv In
|
||||
Positioning <http://ieeexplore.ieee.org/document/637936/>`__
|
||||
|
||||
Dubins path planning
|
||||
--------------------
|
||||
|
||||
A sample code for Dubins path planning.
|
||||
|
||||
|dubins|
|
||||
|
||||
Ref:
|
||||
|
||||
- `Dubins path -
|
||||
Wikipedia <https://en.wikipedia.org/wiki/Dubins_path>`__
|
||||
|
||||
Reeds Shepp planning
|
||||
--------------------
|
||||
|
||||
A sample code with Reeds Shepp path planning.
|
||||
|
||||
|RSPlanning|
|
||||
|
||||
Ref:
|
||||
|
||||
- `15.3.2 Reeds-Shepp
|
||||
Curves <http://planning.cs.uiuc.edu/node822.html>`__
|
||||
|
||||
- `optimal paths for a car that goes both forwards and
|
||||
backwards <https://pdfs.semanticscholar.org/932e/c495b1d0018fd59dee12a0bf74434fac7af4.pdf>`__
|
||||
|
||||
- `ghliu/pyReedsShepp: Implementation of Reeds Shepp
|
||||
curve. <https://github.com/ghliu/pyReedsShepp>`__
|
||||
|
||||
LQR based path planning
|
||||
-----------------------
|
||||
|
||||
A sample code using LQR based path planning for double integrator model.
|
||||
|
||||
|RSPlanning2|
|
||||
|
||||
Optimal Trajectory in a Frenet Frame
|
||||
------------------------------------
|
||||
|
||||
|15|
|
||||
|
||||
This is optimal trajectory generation in a Frenet Frame.
|
||||
|
||||
The cyan line is the target course and black crosses are obstacles.
|
||||
|
||||
The red line is predicted path.
|
||||
|
||||
Ref:
|
||||
|
||||
- `Optimal Trajectory Generation for Dynamic Street Scenarios in a
|
||||
Frenet
|
||||
Frame <https://www.researchgate.net/profile/Moritz_Werling/publication/224156269_Optimal_Trajectory_Generation_for_Dynamic_Street_Scenarios_in_a_Frenet_Frame/links/54f749df0cf210398e9277af.pdf>`__
|
||||
|
||||
- `Optimal trajectory generation for dynamic street scenarios in a
|
||||
Frenet Frame <https://www.youtube.com/watch?v=Cj6tAQe7UCY>`__
|
||||
|
||||
.. |DWA| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/DynamicWindowApproach/animation.gif
|
||||
.. |Dijkstra| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/Dijkstra/animation.gif
|
||||
.. |astar| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/AStar/animation.gif
|
||||
.. |PotentialField| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/PotentialFieldPlanning/animation.gif
|
||||
.. |4| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/ModelPredictiveTrajectoryGenerator/kn05animation.gif
|
||||
.. |5| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/ModelPredictiveTrajectoryGenerator/lookuptable.png?raw=True
|
||||
.. |6| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/StateLatticePlanner/UniformPolarSampling.gif
|
||||
.. |7| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/StateLatticePlanner/BiasedPolarSampling.gif
|
||||
.. |8| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/StateLatticePlanner/LaneSampling.gif
|
||||
.. |PRM| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/ProbabilisticRoadMap/animation.gif
|
||||
.. |VRM| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/VoronoiRoadMap/animation.gif
|
||||
.. |9| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/RRT/animation.gif
|
||||
.. |10| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/RRTstar/animation.gif
|
||||
.. |PythonRobotics| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/RRTDubins/animation.gif
|
||||
.. |AtsushiSakai/PythonRobotics| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/RRTStarDubins/animation.gif
|
||||
.. |11| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/RRTStarReedsShepp/animation.gif
|
||||
.. |irrt| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/InformedRRTStar/animation.gif
|
||||
.. |irrt2| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/BatchInformedRRTStar/animation.gif
|
||||
.. |CLRRT| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/ClosedLoopRRTStar/animation.gif
|
||||
.. |LQRRRT| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/LQRRRTStar/animation.gif
|
||||
.. |12| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/CubicSpline/Figure_1.png?raw=True
|
||||
.. |13| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/CubicSpline/Figure_2.png?raw=True
|
||||
.. |14| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/CubicSpline/Figure_3.png?raw=True
|
||||
.. |B-Spline| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/BSplinePath/Figure_1.png?raw=True
|
||||
.. |eta3| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/Eta3SplinePath/animation.gif?raw=True
|
||||
.. |Bezier1| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/BezierPath/Figure_1.png?raw=True
|
||||
.. |Bezier2| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/BezierPath/Figure_2.png?raw=True
|
||||
.. |2| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/QuinticPolynomialsPlanner/animation.gif
|
||||
.. |dubins| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/DubinsPath/animation.gif?raw=True
|
||||
.. |RSPlanning| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/ReedsSheppPath/animation.gif?raw=true
|
||||
.. |RSPlanning2| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/LQRPlanner/animation.gif?raw=true
|
||||
.. |15| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/FrenetOptimalTrajectory/animation.gif
|
||||
112
docs/modules/path_tracking.rst
Normal file
112
docs/modules/path_tracking.rst
Normal file
@@ -0,0 +1,112 @@
|
||||
.. _path_tracking:
|
||||
|
||||
Path tracking
|
||||
=============
|
||||
|
||||
move to a pose control
|
||||
----------------------
|
||||
|
||||
This is a simulation of moving to a pose control
|
||||
|
||||
|2|
|
||||
|
||||
Ref:
|
||||
|
||||
- `P. I. Corke, "Robotics, Vision and Control" \| SpringerLink
|
||||
p102 <https://link.springer.com/book/10.1007/978-3-642-20144-8>`__
|
||||
|
||||
Pure pursuit tracking
|
||||
---------------------
|
||||
|
||||
Path tracking simulation with pure pursuit steering control and PID
|
||||
speed control.
|
||||
|
||||
|3|
|
||||
|
||||
The red line is a target course, the green cross means the target point
|
||||
for pure pursuit control, the blue line is the tracking.
|
||||
|
||||
Ref:
|
||||
|
||||
- `A Survey of Motion Planning and Control Techniques for Self-driving
|
||||
Urban Vehicles <https://arxiv.org/abs/1604.07446>`__
|
||||
|
||||
Stanley control
|
||||
---------------
|
||||
|
||||
Path tracking simulation with Stanley steering control and PID speed
|
||||
control.
|
||||
|
||||
|4|
|
||||
|
||||
Ref:
|
||||
|
||||
- `Stanley: The robot that won the DARPA grand
|
||||
challenge <http://robots.stanford.edu/papers/thrun.stanley05.pdf>`__
|
||||
|
||||
- `Automatic Steering Methods for Autonomous Automobile Path
|
||||
Tracking <https://www.ri.cmu.edu/pub_files/2009/2/Automatic_Steering_Methods_for_Autonomous_Automobile_Path_Tracking.pdf>`__
|
||||
|
||||
Rear wheel feedback control
|
||||
---------------------------
|
||||
|
||||
Path tracking simulation with rear wheel feedback steering control and
|
||||
PID speed control.
|
||||
|
||||
|5|
|
||||
|
||||
Ref:
|
||||
|
||||
- `A Survey of Motion Planning and Control Techniques for Self-driving
|
||||
Urban Vehicles <https://arxiv.org/abs/1604.07446>`__
|
||||
|
||||
.. _linearquadratic-regulator-(lqr)-steering-control:
|
||||
|
||||
Linear–quadratic regulator (LQR) steering control
|
||||
-------------------------------------------------
|
||||
|
||||
Path tracking simulation with LQR steering control and PID speed
|
||||
control.
|
||||
|
||||
|6|
|
||||
|
||||
Ref:
|
||||
|
||||
- `ApolloAuto/apollo: An open autonomous driving
|
||||
platform <https://github.com/ApolloAuto/apollo>`__
|
||||
|
||||
.. _linearquadratic-regulator-(lqr)-speed-and-steering-control:
|
||||
|
||||
Linear–quadratic regulator (LQR) speed and steering control
|
||||
-----------------------------------------------------------
|
||||
|
||||
Path tracking simulation with LQR speed and steering control.
|
||||
|
||||
|7|
|
||||
|
||||
Ref:
|
||||
|
||||
- `Towards fully autonomous driving: Systems and algorithms - IEEE
|
||||
Conference
|
||||
Publication <http://ieeexplore.ieee.org/document/5940562/>`__
|
||||
|
||||
Model predictive speed and steering control
|
||||
-------------------------------------------
|
||||
|
||||
Path tracking simulation with iterative linear model predictive speed
|
||||
and steering control.
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<img src="https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathTracking/model_predictive_speed_and_steer_control/animation.gif" width="640">
|
||||
|
||||
Ref:
|
||||
|
||||
- `notebook <https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/model_predictive_speed_and_steer_control/notebook.ipynb>`__
|
||||
|
||||
.. |2| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathTracking/move_to_pose/animation.gif
|
||||
.. |3| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathTracking/pure_pursuit/animation.gif
|
||||
.. |4| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathTracking/stanley_controller/animation.gif
|
||||
.. |5| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathTracking/rear_wheel_feedback/animation.gif
|
||||
.. |6| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathTracking/lqr_steer_control/animation.gif
|
||||
.. |7| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathTracking/lqr_speed_steer_control/animation.gif
|
||||
104
docs/modules/slam.rst
Normal file
104
docs/modules/slam.rst
Normal file
@@ -0,0 +1,104 @@
|
||||
.. _slam:
|
||||
|
||||
SLAM
|
||||
====
|
||||
|
||||
Simultaneous Localization and Mapping(SLAM) examples
|
||||
|
||||
.. _iterative-closest-point-(icp)-matching:
|
||||
|
||||
Iterative Closest Point (ICP) Matching
|
||||
--------------------------------------
|
||||
|
||||
This is a 2D ICP matching example with singular value decomposition.
|
||||
|
||||
It can calculate a rotation matrix and a translation vector between
|
||||
points to points.
|
||||
|
||||
|3|
|
||||
|
||||
Ref:
|
||||
|
||||
- `Introduction to Mobile Robotics: Iterative Closest Point Algorithm`_
|
||||
|
||||
EKF SLAM
|
||||
--------
|
||||
|
||||
This is an Extended Kalman Filter based SLAM example.
|
||||
|
||||
The blue line is ground truth, the black line is dead reckoning, the red
|
||||
line is the estimated trajectory with EKF SLAM.
|
||||
|
||||
The green crosses are estimated landmarks.
|
||||
|
||||
|4|
|
||||
|
||||
Ref:
|
||||
|
||||
- `PROBABILISTIC ROBOTICS`_
|
||||
|
||||
FastSLAM 1.0
|
||||
------------
|
||||
|
||||
This is a feature based SLAM example using FastSLAM 1.0.
|
||||
|
||||
The blue line is ground truth, the black line is dead reckoning, the red
|
||||
line is the estimated trajectory with FastSLAM.
|
||||
|
||||
The red points are particles of FastSLAM.
|
||||
|
||||
Black points are landmarks, blue crosses are estimated landmark
|
||||
positions by FastSLAM.
|
||||
|
||||
|5|
|
||||
|
||||
Ref:
|
||||
|
||||
- `PROBABILISTIC ROBOTICS`_
|
||||
|
||||
- `SLAM simulations by Tim Bailey`_
|
||||
|
||||
FastSLAM 2.0
|
||||
------------
|
||||
|
||||
This is a feature based SLAM example using FastSLAM 2.0.
|
||||
|
||||
The animation has the same meanings as one of FastSLAM 1.0.
|
||||
|
||||
|6|
|
||||
|
||||
Ref:
|
||||
|
||||
- `PROBABILISTIC ROBOTICS`_
|
||||
|
||||
- `SLAM simulations by Tim Bailey`_
|
||||
|
||||
Graph based SLAM
|
||||
----------------
|
||||
|
||||
This is a graph based SLAM example.
|
||||
|
||||
The blue line is ground truth.
|
||||
|
||||
The black line is dead reckoning.
|
||||
|
||||
The red line is the estimated trajectory with Graph based SLAM.
|
||||
|
||||
The black stars are landmarks for graph edge generation.
|
||||
|
||||
|7|
|
||||
|
||||
Ref:
|
||||
|
||||
- `A Tutorial on Graph-Based SLAM`_
|
||||
|
||||
.. _`Introduction to Mobile Robotics: Iterative Closest Point Algorithm`: https://cs.gmu.edu/~kosecka/cs685/cs685-icp.pdf
|
||||
.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/
|
||||
.. _SLAM simulations by Tim Bailey: http://www-personal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm
|
||||
.. _A Tutorial on Graph-Based SLAM: http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf
|
||||
|
||||
.. |3| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/SLAM/iterative_closest_point/animation.gif
|
||||
.. |4| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/SLAM/EKFSLAM/animation.gif
|
||||
.. |5| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/SLAM/FastSLAM1/animation.gif
|
||||
.. |6| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/SLAM/FastSLAM2/animation.gif
|
||||
.. |7| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/SLAM/GraphBasedSLAM/animation.gif
|
||||
Reference in New Issue
Block a user