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# PythonRobotics
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Python codes for robotics algorithm.
# Table of Contents
* [What is this?](#what-is-this)
* [Requirements](#requirements)
* [Documentation](#documentation)
* [How to use](#how-to-use)
* [Localization](#localization)
* [Extended Kalman Filter localization](#extended-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)
* [Lidar to grid map](#lidar-to-grid-map)
* [k-means object clustering](#k-means-object-clustering)
* [Rectangle fitting](#rectangle-fitting)
* [SLAM](#slam)
* [Iterative Closest Point (ICP) Matching](#iterative-closest-point-icp-matching)
* [FastSLAM 1.0](#fastslam-10)
* [Path Planning](#path-planning)
* [Dynamic Window Approach](#dynamic-window-approach)
* [Grid based search](#grid-based-search)
* [Dijkstra algorithm](#dijkstra-algorithm)
* [A* algorithm](#a-algorithm)
* [D* algorithm](#d-algorithm)
* [D* Lite algorithm](#d-lite-algorithm)
* [Potential Field algorithm](#potential-field-algorithm)
* [Grid based coverage path planning](#grid-based-coverage-path-planning)
* [State Lattice Planning](#state-lattice-planning)
* [Biased polar sampling](#biased-polar-sampling)
* [Lane sampling](#lane-sampling)
* [Probabilistic Road-Map (PRM) planning](#probabilistic-road-map-prm-planning)
* [Rapidly-Exploring Random Trees (RRT)](#rapidly-exploring-random-trees-rrt)
* [RRT*](#rrt)
* [RRT* with reeds-shepp path](#rrt-with-reeds-shepp-path)
* [LQR-RRT*](#lqr-rrt)
* [Quintic polynomials planning](#quintic-polynomials-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)
* [Stanley control](#stanley-control)
* [Rear wheel feedback control](#rear-wheel-feedback-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)
* [Nonlinear Model predictive control with C-GMRES](#nonlinear-model-predictive-control-with-c-gmres)
* [Arm Navigation](#arm-navigation)
* [N joint arm to point control](#n-joint-arm-to-point-control)
* [Arm navigation with obstacle avoidance](#arm-navigation-with-obstacle-avoidance)
* [Aerial Navigation](#aerial-navigation)
* [drone 3d trajectory following](#drone-3d-trajectory-following)
* [rocket powered landing](#rocket-powered-landing)
* [Bipedal](#bipedal)
* [bipedal planner with inverted pendulum](#bipedal-planner-with-inverted-pendulum)
* [License](#license)
* [Use-case](#use-case)
* [Contribution](#contribution)
* [Citing](#citing)
* [Support](#support)
* [Sponsors](#sponsors)
* [JetBrains](#JetBrains)
* [1Password](#1password)
* [Authors](#authors)
# What is this?
This is a Python code collection of robotics algorithms.
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](https://arxiv.org/abs/1808.10703) ([BibTeX](https://github.com/AtsushiSakai/PythonRoboticsPaper/blob/master/python_robotics.bib))
# Requirements
For running each sample code:
- [Python 3.12.x](https://www.python.org/)
- [NumPy](https://numpy.org/)
- [SciPy](https://scipy.org/)
- [Matplotlib](https://matplotlib.org/)
- [cvxpy](https://www.cvxpy.org/)
For development:
- [pytest](https://pytest.org/) (for unit tests)
- [pytest-xdist](https://pypi.org/project/pytest-xdist/) (for parallel unit tests)
- [mypy](http://mypy-lang.org/) (for type check)
- [sphinx](https://www.sphinx-doc.org/) (for document generation)
- [pycodestyle](https://pypi.org/project/pycodestyle/) (for code style check)
# Documentation
This README only shows some examples of this project.
If you are interested in other examples or mathematical backgrounds of each algorithm,
You can check the full documentation online: [Welcome to PythonRobotics’s documentation\! — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/index.html)
All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation gifs of PythonRobotics](https://github.com/AtsushiSakai/PythonRoboticsGifs)
# How to use
1. Clone this repo.
```terminal
git clone https://github.com/AtsushiSakai/PythonRobotics.git
```
2. Install the required libraries.
- using conda :
```terminal
conda env create -f requirements/environment.yml
```
- using pip :
```terminal
pip install -r requirements/requirements.txt
```
3. Execute python script in each directory.
4. Add star to this repo if you like it :smiley:.
# Localization
## Extended Kalman Filter localization
<img src="https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/extended_kalman_filter/animation.gif" width="640" alt="EKF pic">
Ref:
- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.html)
## Particle filter localization
![2](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/particle_filter/animation.gif)
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 an estimated trajectory with PF.
It is assumed that the robot can measure a distance from landmarks (RFID).
These measurements are used for PF localization.
Ref:
- [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/)
## Histogram filter localization
![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/histogram_filter/animation.gif)
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.
![2](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/gaussian_grid_map/animation.gif)
## Ray casting grid map
This is a 2D ray casting grid mapping example.
![2](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/raycasting_gr
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### 机器人算法示例描述 #### A*算法概述 A*算法是一种启发式搜索算法,广泛应用于机器人导航和路径规划。它结合了路径的已知成本和启发式估计的未来成本,从而高效地找到从起点到终点的最短路径。 #### 算法步骤 1. **初始化**: - 创建一个优先队列 `open_set`,用于存储待探索的节点,初始时将起点加入队列。 - 定义两个字典 `g_score` 和 `f_score`,用于记录从起点到各节点的实际成本和总估计成本(包括启发式估计)。 - 创建一个字典 `came_from`,用于记录路径。 2. **启发式函数**: - 定义一个启发式函数(如曼哈顿距离),用于估算从当前节点到目标节点的成本。 3. **主循环**: - 从 `open_set` 中取出优先级最高(估计总成本最低)的节点 `current`。 - 如果 `current` 是目标节点,则重构路径并返回。 - 否则,将 `current` 的所有相邻节点(即邻居)加入 `open_set`,并计算其 `g_score` 和 `f_score`。
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