https://github.com/aaronjs99/squat-plan

SQuAT Plan: Smooth Quadrotor Agile Trajectory Planning
https://github.com/aaronjs99/squat-plan

kinematics model-predictive-control motion-planning optimal-control python robot-simulation robotics trajectory-planning

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SQuAT Plan: Smooth Quadrotor Agile Trajectory Planning

• Host: GitHub
• URL: https://github.com/aaronjs99/squat-plan
• Owner: aaronjs99
• License: mit
• Created: 2022-10-20T21:39:33.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2025-07-16T00:16:37.000Z (11 months ago)
• Last Synced: 2025-09-08T22:25:26.063Z (9 months ago)
• Topics: kinematics, model-predictive-control, motion-planning, optimal-control, python, robot-simulation, robotics, trajectory-planning
• Language: Python
• Homepage:
• Size: 1.39 MB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# SQuAT Plan: Smooth Quadrotor Agile Trajectory Planning

![Python](https://img.shields.io/badge/python-3.9+-blue?logo=python)

![License](https://img.shields.io/badge/license-MIT-green)

![Build](https://img.shields.io/badge/build-passing-brightgreen)

![Platform](https://img.shields.io/badge/platform-WSL--Debian%20%7C%20ROS-lightgrey)

**SQuAT Plan** is a Python-based framework for agile trajectory planning of quadrotors navigating through complex environments. It integrates nonlinear optimization (via GEKKO), obstacle avoidance, and both 3D and ROS-based visualizations.

## Project Structure

```

squat-plan/

├── run.py                          # Unified entry point

├── pyproject.toml                  # Modern build system config

├── src/

│   └── squatplan/

│       ├── __init__.py

│       ├── main.py                 # Core simulation runner

│       ├── trajopt.py              # Trajectory optimization logic

│       ├── plotter.py              # Matplotlib-based plotting

│       ├── quaternion.py           # Quaternion math utils

│       ├── forester.py             # Obstacle generation

│       └── sphere_example_rviz.py  # ROS RViz marker publishing

├── squat.rviz                      # RViz display config

├── LICENSE

├── README.md

└── presentation.pdf                # MAE 271D presentation

```

## Features

- **Trajectory optimization** using GEKKO with full or simplified dynamics

- **Obstacle avoidance** using geometric constraints

- **Quaternion-based orientation modeling**

- **3D visualizations** via Matplotlib and RViz

- **Synthetic forest generation** for randomized path planning scenarios

## Getting Started

### Dependencies

Clone the repo and install in editable mode:

```bash

git clone https://github.com/aaronjohnsabu1999/squat-plan.git

cd squat-plan

python3 -m venv .venv

source venv/bin/activate

pip install -e .[dev]

```

Install ROS dependencies if on Linux or a WSL:

```bash

sudo apt install ros-${ROS_DISTRO}-rospy \

                 ros-${ROS_DISTRO}-geometry-msgs \

                 ros-${ROS_DISTRO}-visualization-msgs

```

### Run Simulation

```bash

python run.py

```

To launch RViz in parallel:

```bash

roscore

# Then in another terminal:

python src/squatplan/sphere_example_rviz.py

```

## Output Example

- **Trajectory** and **state evolution** plots (position, velocity, quaternion, thrust, moments)

- **3D environment** with spherical/cylindrical obstacles and path trajectory

## Project Context

> Developed as a final project for MAE 271D — *Control and Trajectory Planning for Autonomous Aerial Systems* at UCLA.

**Contributors:**

- Aaron John Sabu  

- Ryan Nemiroff  

- Brett T. Lopez *(Instructor)*

Contact: `{aaronjs, ryguyn, btlopez}@ucla.edu`

## License

MIT License © 2025  

University of California, Los Angeles