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isaac_drone_racer

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Isaac Drone Racer is a reinforcement learning framework for high speed autonomous drone racing, built on top of IsaacLab

파일 탐색기

최종 버전 다운로드 (.zip)

- - pre-commit.yaml
- LICENSE_HEADER.txt
- - settings.template.json
  - setup_vscode.py
- .gitignore
- extensions.json
- launch.json
- tasks.json
- - 5_in_drone.usd
- - gate.glb
  - gate.usd
- __init__.py
- cf2x.py
- five_in_drone.py
- __init__.py
- allocation.py
- motor.py
- motion_trace1.jpg
- motion_trace2.jpg
- motion_trace3.jpg
- - play.py
  - train.py
- test.py
- - __init__.py
  - drone_racer_env_cfg.py
  - track_generator.py
- __init__.py
- test_dynamics.py
- __init__.py
- logger.py
- plotter.py
.flake8
.gitattributes
.gitignore
.pre-commit-config.yaml
extension.toml
LICENSE
pyproject.toml
README.md
setup.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/kousheekc/isaac_drone_racer

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd isaac_drone_racer

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip3 install -e .

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

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![Isaac Drone Racer](https://raw.githubusercontent.com/kousheekc/isaac_drone_racer/master/media/motion_trace1.jpg)

---

# Isaac Drone Racer

[![IsaacSim](https://img.shields.io/badge/IsaacSim-4.5.0-silver.svg)](https://docs.isaacsim.omniverse.nvidia.com/latest/index.html)
[![Python](https://img.shields.io/badge/python-3.10-blue.svg)](https://docs.python.org/3/whatsnew/3.10.html)
[![pre-commit](https://img.shields.io/github/actions/workflow/status/isaac-sim/IsaacLab/pre-commit.yaml?logo=pre-commit&logoColor=white&label=pre-commit&color=brightgreen)](https://github.com/kousheekc/isaac_drone_racer/blob/master/.github/workflows/pre-commit.yaml)
[![License](https://img.shields.io/badge/license-BSD--3-yellow.svg)](https://opensource.org/licenses/BSD-3-Clause)

**Isaac Drone Racer** is an open-source simulation framework for autonomous drone racing, developed on top of [IsaacLab](https://github.com/isaac-sim/IsaacLab). It is designed for training reinforcement learning policies in realistic racing environments, with a focus on accurate physics and modular design.

Autonomous drone racing is an active area of research. This project builds on insights from that body of work, combining them with massively parallel simulation to train racing policies within minutes — offering a fast and flexible platform for experimentation and benchmarking.

You can watch the related video here: [https://youtu.be/wLTYtpEUEEk](https://youtu.be/wLTYtpEUEEk)

## Features

Key highlights of the Isaac Drone Racer project:

1. **Accurate Physics Modeling** — Simulates rotor dynamics, aerodynamic drag, and power consumption to closely match real-world quadrotor behavior.
2. **Low-Level Flight Controller** — Built-in attitude and rate controllers.
3. **Manager-Based Design** — Modular architecture using IsaacLab's [manager based architecture](https://isaac-sim.github.io/IsaacLab/main/source/refs/reference_architecture/index.html#manager-based).
4. **Onboard Sensor Suite** — Includes simulated fisheye camera, IMU and collision detection.
5. **Track Generator** — Dynamically generate custom race tracks.
6. **Logger and Plotter** — Integrated tools for monitoring and visualizing flight behavior.

## Requirements
This framework has been tested on x64 based Linux systems, specifically Ubuntu 22.04. But it should also work on Windows 10/11.

### Prerequisites
- Workstation capable of running Isaac Sim (see [link](https://github.com/isaac-sim/IsaacSim?tab=readme-ov-file#prerequisites-and-environment-setup))
- [Git](https://git-scm.com/downloads) & [Git LFS](https://git-lfs.com)
- [Conda](https://www.anaconda.com/docs/getting-started/miniconda/install) for local installation or [Docker](https://docs.docker.com/engine/install/ubuntu/) with [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)

## Setup
1. Follow the [Isaac Lab pip installation instructions](https://isaac-sim.github.io/IsaacLab/main/source/setup/installation/pip_installation.html), with the following modifications:
- After cloning the Isaac Lab repository:
```bash
git clone git@github.com:isaac-sim/IsaacLab.git
```

- Checkout the `v2.1.0` release tag:
```bash
cd IsaacLab
git checkout v2.1.0
```

2. Clone Isaac Drone Racer:
```bash
git clone https://github.com/kousheekc/isaac_drone_racer.git
```

3. Install the modules in editable mode
```bash
cd isaac_drone_racer
pip3 install -e .
```

## Usage
The drone racing task is registered as a standard Gym environment with the ID: `Isaac-Drone-Racer-v0`. Training and evaluation are powered by the [skrl](https://github.com/Toni-SM/skrl) library.

### Training a Policy

To train a policy, run the following command from the root of the `isaac_drone_racer` repository. This will launch the simulation in headless mode with 4096 agents running parallelly.

```bash
python3 scripts/rl/train.py --task Isaac-Drone-Racer-v0 --headless --num_envs 4096
```

> [!NOTE]
>    You can pass additional CLI arguments supported by the [AppLauncher](https://isaac-sim.github.io/IsaacLab/main/source/tutorials/00_sim/launch_app.html). Additionally since IsaacLab supports the [Hydra Configuration System](https://isaac-sim.github.io/IsaacLab/main/source/features/hydra.html), task specific parameters can be adjusted from CLI.
>    For example, to disable the motor model during training:
>   ```bash
>   python3 scripts/rl/train.py --task Isaac-Drone-Racer-v0 --headless --num_envs 4096 env.actions.control_action.use_motor_model=False
>   ```

### Playing Back a Trained Policy
To run a trained policy in the simulator:

```bash
python3 scripts/rl/play.py --task Isaac-Drone-Racer-Play-v0 --num_envs 1
```

This will launch a single agent with the latest checkpoint and play the trained policy in the racing environment. All the same CLI and Hydra configuration options used during training are supported here as well.

## Next Steps

- [ ] **Data-driven aerodynamic model pipeline** - integrate tools for data driven system identification, calibration and include the learned aerodynamic forces into the simulation environment.
- [ ] **Power consumption model**  - incorporate a detailed power model that accounts for battery discharge based on current draw.
- [ ] **Policy learning using onboard sensors** - explore and implement methods to transition away from full-state observations by instead using only onboard sensor data (e.g camera + IMU).

## Troubleshooting
- When running a workflow script, ensure that the IsaacLab conda environment is active:
```bash
conda activate env_isaaclab
```
- When launching Isaac Sim for the first time, it may take a significant amount of time to load (potentially 10 minutes). This is normal, please be patient.

## Acknowledgement

- **Kaufmann, E., Bauersfeld, L., Loquercio, A., Müller, M., Koltun, V., & Scaramuzza, D.** (2023).
  *Champion-level drone racing using deep reinforcement learning*.
  [https://doi.org/10.1038/s41586-023-06419-4](https://doi.org/10.1038/s41586-023-06419-4)

- **Rudin, N., Hoeller, D., Reist, P., & Hutter, M.** (2022).
  *Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning*.
  [arXiv:2109.11978](https://arxiv.org/abs/2109.11978)

- **Ferede, R., De Wagter, C., Izzo, D., & de Croon, G. C. H. E.** (2024).
  *End-to-end Reinforcement Learning for Time-Optimal Quadcopter Flight*.
  [https://doi.org/10.1109/ICRA57147.2024.10611665](https://doi.org/10.1109/ICRA57147.2024.10611665)

## License
This project is licensed under the BSD 3-Clause License - see the [LICENSE](https://github.com/kousheekc/isaac_drone_racer/blob/master/LICENSE) file for details.

## Contact
Kousheek Chakraborty - kousheekc@gmail.com

Project Link: [https://github.com/kousheekc/isaac_drone_racer](https://github.com/kousheekc/isaac_drone_racer)

If you encounter any difficulties, feel free to reach out through the Issues section. If you find any bugs or have improvements to suggest, don't hesitate to make a pull request.