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path_planning's Introduction

path_planning

path_planning library provides implementations of various path planning algorithms, including RRT and RRT*. More algorithms will be added in the future.

Getting Started

To use the library, simply follow these steps:

  1. Clone the repository to your local machine:

    git clone https://github.com/bxtbold/path_planning.git
    
  2. Install the required dependencies:

    pip install -r requirements.txt
    
  3. Run the path planning algorithms:

    python3 main.py

Configuration

You can customize the behavior of the path planning algorithms by editing the config.yaml file in the repo directory. Here are the available options:

  • planner: Specifies the planner to use. Options: RRT, RRT*.
  • dimensions: Specifies the dimensions of the space. Options: 2, 3.
  • domain: Specifies the length of the domain for each dimension. (For example, if you set the domain parameter to 50, in a 2D space, the domain would be represented as (50, 50). In a 3D space, the domain would be represented as (50, 50, 50))
  • k: The maximum number of iterations for the algorithm.
  • step: The step size for expanding the tree.
  • use_obstacle: Specifies whether to include obstacles in the environment. Options: true, false.
  • should_visualize: Specifies whether to visualize the path planning process. Options: true, false.

Example Usage

Here's an example configuration in config.yaml:

path_planning:
  planner: RRT*
  dimensions: 2
  domain: 50
  k: 1000
  step: 5
  use_obstacle: false
  should_visualize: true

This configuration will use the RRT* planner in a 2D space with a domain size of 50x50, running for a maximum of 1000 iterations with a step size of 5. Obstacles will not be included, and visualization will be enabled.

Example 2D and 3D path planning visualizations

Feel free to adjust the configuration parameters according to your specific needs.

Contribution

Contributions to this project are welcome! If you have ideas for improvements or new features, please submit a pull request or open an issue on GitHub.

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