mirror of
https://github.com/AtsushiSakai/PythonRobotics.git
synced 2026-01-13 19:08:06 -05:00
update README link
This commit is contained in:
Atsushi Sakai
80
README.md
80
README.md
@@ -1,4 +1,4 @@
|
||||
<img src="https://github.com/AtsushiSakai/PythonRobotics/blob/master/icon.png?raw=true" align="right" width="300"/>
|
||||
<img src="https://github.com/AtsushiSakai/PythonRobotics/raw/master/icon.png?raw=true" align="right" width="300"/>
|
||||
|
||||
# PythonRobotics
|
||||
[](https://travis-ci.org/AtsushiSakai/PythonRobotics)
|
||||
@@ -98,7 +98,7 @@ The red ellipse is estimated covariance ellipse with EKF.
|
||||
|
||||
## Unscented Kalman Filter localization
|
||||
|
||||
|
||||
|
||||
|
||||
This is a sensor fusion localization with Unscented Kalman Filter(UKF).
|
||||
|
||||
@@ -110,7 +110,7 @@ Ref:
|
||||
|
||||
## Particle Filter localization
|
||||
|
||||
|
||||
|
||||
|
||||
This is a sensor fusion localization with Particle Filter(PF).
|
||||
|
||||
@@ -128,13 +128,13 @@ This measurements are used for PF localization.
|
||||
|
||||
This is a 2D gaussian grid mapping example.
|
||||
|
||||
|
||||
|
||||
|
||||
## Ray casting grid map
|
||||
|
||||
This is a 2D ray casting grid mapping example.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# SLAM
|
||||
@@ -147,7 +147,7 @@ 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:
|
||||
|
||||
@@ -162,7 +162,7 @@ The blue line is ground truth, the black line is dead reckoning, the red line is
|
||||
|
||||
The green cross are estimated landmarks.
|
||||
|
||||
|
||||
|
||||
|
||||
Ref:
|
||||
|
||||
@@ -176,7 +176,7 @@ 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
|
||||
@@ -185,7 +185,7 @@ This is a 2D navigation sample code with Dynamic Window Approach.
|
||||
|
||||
This is a 2D grid based shortest path planning with Dijkstra's algorithm.
|
||||
|
||||
|
||||
|
||||
|
||||
In the animation, cyan points are searched nodes.
|
||||
|
||||
@@ -193,7 +193,7 @@ In the animation, cyan points are searched nodes.
|
||||
|
||||
This is a 2D grid based shortest path planning with A star algorithm.
|
||||
|
||||
|
||||
|
||||
|
||||
In the animation, cyan points are searched nodes.
|
||||
|
||||
@@ -203,7 +203,7 @@ It's heuristic is 2D Euclid distance.
|
||||
|
||||
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.
|
||||
|
||||
@@ -220,11 +220,11 @@ This algorithm is used for state lattice planner.
|
||||
|
||||
### Path optimization sample
|
||||
|
||||
|
||||
|
||||
|
||||
### Lookup table generation sample
|
||||
|
||||
|
||||
|
||||
|
||||
Ref:
|
||||
|
||||
@@ -240,21 +240,21 @@ This code uses the model predictive trajectory generator to solve boundary probl
|
||||
|
||||
### Uniform polar sampling
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
### Biased polar sampling
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
### Lane sampling
|
||||
|
||||
|
||||
|
||||
|
||||
## Probabilistic Road-Map (PRM) planning
|
||||
|
||||
|
||||
|
||||
|
||||
This PRM planner uses Dijkstra method for graph search.
|
||||
|
||||
@@ -271,7 +271,7 @@ Ref:
|
||||
|
||||
## Voronoi Road-Map planning
|
||||
|
||||
|
||||
|
||||
|
||||
This Voronoi road-map planner uses Dijkstra method for graph search.
|
||||
|
||||
@@ -290,7 +290,7 @@ Ref:
|
||||
|
||||
### Basic RRT
|
||||
|
||||
|
||||
|
||||
|
||||
This script is a simple path planning code with Rapidly-Exploring Random Trees (RRT)
|
||||
|
||||
@@ -298,7 +298,7 @@ Black circles are obstacles, green line is a searched tree, red crosses are star
|
||||
|
||||
### RRT\*
|
||||
|
||||
|
||||
|
||||
|
||||
This script is a path planning code with RRT\*
|
||||
|
||||
@@ -313,21 +313,21 @@ Ref:
|
||||
|
||||
### 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.
|
||||
|
||||
@@ -335,7 +335,7 @@ Path planning for a car robot with RRT\* and reeds sheep path planner.
|
||||
|
||||
A vehicle model based path planning with closed loop RRT\*.
|
||||
|
||||
|
||||
|
||||
|
||||
In this code, pure-pursuit algorithm is used for steering control,
|
||||
|
||||
@@ -359,14 +359,14 @@ This code generates a curvature continuous path based on x-y waypoints with cubi
|
||||
|
||||
Heading angle of each point can be also calculated analytically.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## B-Spline planning
|
||||
|
||||
|
||||
|
||||
|
||||
This is a path planning with B-Spline curse.
|
||||
|
||||
@@ -384,13 +384,13 @@ 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:
|
||||
@@ -401,7 +401,7 @@ Ref:
|
||||
|
||||
Motion planning with quintic polynomials.
|
||||
|
||||
|
||||
|
||||
|
||||
It can calculate 2D path, velocity, and acceleration profile based on quintic polynomials.
|
||||
|
||||
@@ -414,7 +414,7 @@ Ref:
|
||||
|
||||
A sample code for Dubins path planning.
|
||||
|
||||
|
||||
|
||||
|
||||
Ref:
|
||||
|
||||
@@ -424,7 +424,7 @@ Ref:
|
||||
|
||||
A sample code with Reeds Shepp path planning.
|
||||
|
||||
|
||||
|
||||
|
||||
Ref:
|
||||
|
||||
@@ -437,7 +437,7 @@ Ref:
|
||||
|
||||
## Optimal Trajectory in a Frenet Frame
|
||||
|
||||
|
||||
|
||||
|
||||
This is optimal trajectory generation in a Frenet Frame.
|
||||
|
||||
@@ -458,7 +458,7 @@ Ref:
|
||||
|
||||
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.
|
||||
|
||||
@@ -470,7 +470,7 @@ Ref:
|
||||
|
||||
Path tracking simulation with Stanley steering control and PID speed control.
|
||||
|
||||
|
||||
|
||||
|
||||
Ref:
|
||||
|
||||
@@ -484,7 +484,7 @@ Ref:
|
||||
|
||||
Path tracking simulation with rear wheel feedback steering control and PID speed control.
|
||||
|
||||
|
||||
|
||||
|
||||
Ref:
|
||||
|
||||
@@ -495,7 +495,7 @@ Ref:
|
||||
|
||||
Path tracking simulation with LQR steering control and PID speed control.
|
||||
|
||||
|
||||
|
||||
|
||||
Ref:
|
||||
|
||||
@@ -506,7 +506,7 @@ Ref:
|
||||
|
||||
Path tracking simulation with LQR speed and steering control.
|
||||
|
||||
|
||||
|
||||
|
||||
Ref:
|
||||
|
||||
@@ -517,7 +517,7 @@ Ref:
|
||||
|
||||
Path tracking simulation with iterative linear model predictive speed and steering control.
|
||||
|
||||
<img src="https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/model_predictive_speed_and_steer_control/animation.gif" width="640">
|
||||
<img src="https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathTracking/model_predictive_speed_and_steer_control/animation.gif" width="640">
|
||||
|
||||
This code uses cvxpy as an optimization modeling tool.
|
||||
|
||||
|
||||
Reference in New Issue
Block a user