How to build the RRT viewer on Windows?

The README provides installation instructions for Ubuntu via apt-get; building on Windows requires manual setup of Qt and other libraries, which can be complex due to platform-specific dependencies.

RRT vs RRT* for robot path planning?

RRT is faster but not optimal, while RRT* converges to the shortest path but is slower. This implementation is basic RRT, so for optimal paths, you'd need to extend the code or use a different library.

How to add custom obstacles to the visualization?

The README doesn't specify obstacle support; you'd likely need to modify the C++ code to integrate obstacle representation, as the viewer primarily focuses on point-to-point planning without built-in obstacle handling.

Can I use this library without the Qt viewer?

Yes, the modular build system allows building only the core RRT library for headless applications, but you must manage the dependencies and CMake configuration accordingly.

What robotics frameworks is this compatible with?

As a C++ library, it can be integrated into frameworks like ROS by linking the library, but the README doesn't provide specific integration examples or documentation for such use cases.

How to tune RRT parameters for faster convergence?

Parameters like step size are likely configurable in the code; you'd need to examine the C++ implementation and adjust them manually, as no high-level configuration options are mentioned in the README.

rrt

NOASSERTIONC++0.6.0

A C++ implementation of Rapidly-exploring Random Tree (RRT) algorithm with an interactive Qt-based viewer.

GitHub

What is rrt?

RoboJackets/rrt is a C++ implementation of the Rapidly-exploring Random Tree (RRT) algorithm, a sampling-based motion planning technique used to find feasible paths in high-dimensional spaces. It solves the problem of path planning for robots and autonomous systems by efficiently exploring unknown environments. The project includes an interactive Qt-based viewer to visualize the algorithm's tree expansion and path discovery in real-time.

Target Audience

Robotics engineers, researchers, and students working on motion planning, autonomous navigation, or algorithm visualization who need a practical, open-source RRT implementation.

Value Proposition

Developers choose this for its clean C++ implementation, interactive visualization tool, and educational focus, making it easier to understand and integrate RRT into robotics projects compared to theoretical papers or minimal code snippets.

Overview

C++ RRT (Rapidly-exploring Random Tree) Implementation

Use Cases

Best For

Implementing RRT for robotic arm path planning

Related Projects

Community-curated · Updated weekly · 100% open source

Found a gem we're missing?

Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.

Submit a project Star on GitHub

263 stars85 forks0 contributors

Educational demonstrations of sampling-based motion planning algorithms

Prototyping autonomous vehicle navigation systems

Visualizing tree expansion in real-time for algorithm debugging

Integrating RRT into C++-based robotics frameworks

Research projects requiring a modifiable RRT implementation

Not Ideal For

Applications requiring optimal or deterministic path planning, as RRT is probabilistic and doesn't guarantee the shortest path.
Embedded or headless systems without Qt support, due to the GUI dependencies for the viewer.
Projects needing rapid prototyping in higher-level languages like Python, since this is a C++ implementation with complex setup.

Pros & Cons

Pros

Interactive Real-time Viewer

The Qt-based GUI allows dragging source and destination points to visualize tree expansion live, making it excellent for educational demonstrations and debugging, as shown in the screenshot in the README.

Performant C++ Core

The RRT algorithm is implemented in C++ for efficiency, enabling integration into robotics applications where performance is critical, as highlighted in the project description.

Educational Resources

Includes links to external learning materials about RRTs, aiding understanding and modification for research or study, as listed in the Resources section.

Modular Build System

Uses CMake and Ninja to build the library and viewer separately, facilitating customization and integration into larger projects, as mentioned in the Key Features.

Cons

Complex Dependency Setup

Requires installing multiple packages like Qt, Eigen, Boost, and FLANN, which can be cumbersome, especially on non-Ubuntu systems, as detailed in the Dependencies section.

Basic Algorithm Only

Focuses on standard RRT without variants like RRT* or bidirectional RRT, limiting its utility for advanced motion planning needs that require optimality or faster convergence.

Platform-Specific Viewer

The viewer relies on Qt and desktop GUI support, making it unsuitable for headless or embedded environments without such dependencies.

Frequently Asked Questions

Home

Robotic Tooling

pinocchio

A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives

Stars3,441

Forks539

Last commit2 days ago

EGO-Planner

EGO-Planner is an ESDF-free gradient-based local planner designed for quadrotor navigation. It significantly reduces computation time compared to state-of-the-art methods by avoiding the computationally expensive Euclidean Signed Distance Field (ESDF) construction, enabling real-time performance with total planning times around 1ms. ## Key Features - **ESDF-Free Planning** — Eliminates the need to compute Euclidean Signed Distance Fields, drastically reducing computational overhead. - **Lightweight Gradient-Based Optimization** — Uses a gradient-based approach for efficient local trajectory generation. - **GPU/CPU Versatility** — Offers both GPU and CPU versions of its local sensing module for depth image generation or pointcloud processing. - **Fast Computation** — Achieves planning times of approximately 1ms, suitable for real-time drone control. - **Simulation-Ready** — Includes a lightweight quadrotor simulator and supports integration with sensors like Intel RealSense for hardware testing. ## Philosophy EGO-Planner prioritizes computational efficiency and real-time performance by removing the ESDF construction bottleneck, making advanced local planning accessible for resource-constrained aerial robotics applications.

Stars2,508

Forks394

Last commit1 year ago

casADi

CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python or Matlab/Octave.

Stars2,223

Forks448

Last commit2 days ago