Welcome to the Motion Planning repository! This repository contains an implementation of motion planning algorithms and simulations to navigate autonomous agents in complex environments.
The goal of this project is to explore various motion planning techniques and algorithms that enable robots or autonomous agents to plan their paths efficiently and safely. The repository includes code, documentation, and examples showcasing different approaches to motion planning.
-
Path Planning Algorithms: The repository provides implementations of popular motion planning algorithms such as A*, RRT (Rapidly-Exploring Random Tree), RRT* (Rapidly-Exploring Random Tree Star), PRM (Probabilistic Roadmap), and more. These algorithms enable the generation of optimal or near-optimal paths in complex environments.
-
Simulation Environments: The project includes simulation environments where you can test and visualize the motion planning algorithms. These environments provide a virtual representation of real-world scenarios and allow you to observe the behavior of autonomous agents as they navigate through obstacles.
- Documentation and Examples: Detailed documentation is available to understand the theoretical concepts behind each motion planning algorithm. Additionally, the repository provides examples and sample code that demonstrate how to use the algorithms effectively in various scenarios.
To get started with the Motion Planning repository, follow these steps:
- Clone the repository:
git clone https://github.com/nikunjparmar828/Motion-Planning.git