Python codes for robotics algorithm.
- What is this?
- Requirements
- How to use
- Localization
- Mapping
- SLAM
- Path Planning
- Dynamic Window Approach
- Grid based search
- Model Predictive Trajectory Generator
- State Lattice Planning
- Probabilistic Road-Map (PRM) planning
- Voronoi Road-Map planning
- Rapidly-Exploring Random Trees (RRT)
- Cubic spline planning
- B-Spline planning
- Bezier path planning
- Quintic polynomials planning
- Dubins path planning
- Reeds Shepp planning
- LQR based path planning
- Optimal Trajectory in a Frenet Frame
- Path tracking
- License
- Contribution
- Support
- Authors
This is a Python code collection of robotics algorithms, especially autonomous navigation.
Feature:
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Widely used and practical algorithms are selected.
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Minimum dependency.
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Easy to read for understanding each algorithm's basic idea.
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Python 3.6.x
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numpy
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scipy
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matplotlib
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pandas
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Install the required libraries.
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Clone this repo.
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Execute python script in each directory.
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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.
