Path Planning Algorithms and Visualization in C++

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Overview

This repository contains the C++ Implementation of Path Planning Algorithms of some common path planning algorithms used in robotics, including Search-based algorithms and Sampling-based algorithms based on the Python Implementation by Huiming Zhou. The algorithms can be visualized using the custom plotting class, which includes the generation of a new environment during each run for the grid-based planners, and an interactive dynamic environment developed to showcase dynamic algorithms. The related papers are listed in ZHM-REAL's original Python repository Papers.

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Dependencies

The following dependencies are required to build and run the programs:

• OpenCV 2.4 or later: Install OpenCV from https://opencv.org/releases/ or use your package manager.
• ImageMagick++ (Magick++): Install ImageMagick++ from the official ImageMagick website or use your package manager. On many systems, installing libmagick++-dev (for Debian/Ubuntu) using sudo apt-get install libmagick++-dev or ImageMagick-c++-devel (for Red Hat/Fedora) using sudo dnf install ImageMagick-c++-devel should suffice.

Please make sure you have installed the required dependencies before compiling and running the programs.

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Compilation and Build

Clone the repository:

git clone https://github.com/ahmedadamji/PathPlanning.git
cd PathPlanning

Make sure you have CMake installed. If not, install it from https://cmake.org/download/.

Create a build directory and navigate into it:

mkdir build
cd build

Run CMake to configure the build:

cmake ..

Build the project:

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Running the Programs

Search_2D

To run the search-based algorithms (A*, Dijkstra, Best-First, etc.):

Open the terminal and navigate to the build directory.

Run the executable for the search_2D program:

./PathPlanning

The program will visualize the path planning algorithms on a 2D grid environment.

RRT_2D

To run the sampling-based algorithm (RRT-Connect):

Open the terminal and navigate to the build directory.

Run the executable for the rrt_2D program:

./SamplingBasedPlanning2D

The program will visualize the RRT-Connect algorithm on a 2D grid environment.

Note: In each main file, you can comment/uncomment the specific algorithm instantiation to choose which algorithm to run.

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Animations - Search Based Planning

BFS

DFS

Dijkstra

Best-First Search

A* Search

Repeated A* Search

Bidirectional A* Search

ARA* Search

LRTA* Search

RTAA* Search

Animations - Sampling-Based

RRT 2D

Goal Biased RRT

RRT Connect

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Acknowledgments

Thanks to Huiming Zhou for providing the original Python implementation and related papers for the path planning algorithms.