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Python codes for robotics algorithm.
This is a Python code collection of robotics algorithms, especially autonomous navigation.
Feature:
Widely used and practical algorithms are selected.
Minimum dependency.
Easy to read for understanding each algorithm's basic idea.
Python 3.6.x
numpy
scipy
matplotlib
pandas
Install the required libraries.
Clone this repo.
Execute python script in each directory.
Add star to this repo if you like it
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:
This is a sensor fusion localization with Unscented Kalman Filter(UKF).
The lines and points are same meaning of the EKF simulation.
Ref:
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:
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:
This is a 2D Gaussian grid mapping example.
This is a 2D ray casting grid mapping example.
This is a 2D object clustering with k-means algorithm.
This is a 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.
Simultaneous Localization and Mapping(SLAM) examples
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:
This is a 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 cross are estimated landmarks.
Ref:
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:
This is a feature based SLAM example using FastSLAM 2.0.
The animation has same meanings as one of FastSLAM 1.0.
Ref:
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:
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.
This is a 2D grid based shortest path planning with A star algorithm.
In the animation, cyan points are searched nodes.
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.
Ref:
This is a path optimization sample on model predictive trajectory generator.
This algorithm is used for state lattice planner.
Ref:
This script is a path planning code with state lattice planning.
This code uses the model predictive trajectory generator to solve boundary problem.
Ref:
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:
This Voronoi road-map planner uses Dijkstra method for graph search.
In the animation, blue points are Voronoi points,
Cyan crosses means searched points with Dijkstra method,
The red line is the final path of Vornoi Road-Map.
Ref:
This script 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.
This script 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:
Path planning for a car robot with RRT and dubins path planner.
Path planning for a car robot with RRT* and dubins path planner.
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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,
PID is used for speed control.
Ref:
Motion Planning in Complex Environments using Closed-loop Prediction
Real-time Motion Planning with Applications to Autonomous Urban Driving
[1601.06326] Sampling-based Algorithms for Optimal Motion Planning Using Closed-loop Prediction
This is a path planning simulation with LQR-RRT*.
A double integrator motion model is used for LQR local planner.
Ref:
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.
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:
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:
Motion planning with quintic polynomials.
It can calculate 2D path, velocity, and acceleration profile based on quintic polynomials.
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A sample code for Dubins path planning.
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A sample code with Reeds Shepp path planning.
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A sample code using LQR based path planning for double integrator model.
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
Optimal trajectory generation for dynamic street scenarios in a Frenet Frame
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.
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Path tracking simulation with Stanley steering control and PID speed control.
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Path tracking simulation with rear wheel feedback steering control and PID speed control.
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Path tracking simulation with LQR steering control and PID speed control.
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Path tracking simulation with LQR speed and steering control.
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Path tracking simulation with iterative linear model predictive speed and steering control.
This code uses cvxpy as an optimization modeling tool.
Ref:
MIT
If you have any quesions or problems about this code, feel free to add an issue.
A small PR like bug fix is welcome.
If your PR is merged multiple times, I will add your account to the author list.
You can support financially this project via PayPal.
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