diff --git a/docs/challenge/deployment.md b/docs/challenge/deployment.md index 5523164ff..e509f10eb 100644 --- a/docs/challenge/deployment.md +++ b/docs/challenge/deployment.md @@ -9,42 +9,51 @@ The idea for the deployment is to have an environment that exactly matches the i We use a [Vicon](https://www.vicon.com/) motion tracking system to track the motion of the drone. The Vicon system consists of several cameras that are placed around the track, and a base station that calculates object poses by triangulation. Gates, obstacles and the drone are all equipped with reflective markers, which can be tracked by the cameras. Since we'd need to resort to numerical differentiation to get velocity information, we're running state estimators that filter the noisy Vicon measurements and provide smoother estimates of the drone's state. -As mentioned in the [Installation and Setup](../getting_started/setup.md) section, you need to run two terminals to launch a ROS2 node with the `motion_capture_tracking` package that make the Vicon poses available: +After completing the [hardware setup](../getting_started/setup.md#hardware-setup-ubuntu-only), you need a total of *three* open terminals in the repository to deploy your controller. The commands below use the deploy environment. + +In the first terminal, run this command to launch the translation layer from the motion capture cameras to ROS2. ```bash -pixi shell -e deploy -ros2 launch motion_capture_tracking launch.py +pixi run -e deploy mocap ``` !!! warning If you cannot see the drone in RVIZ, it is likely that Vicon is not turned on, or the drone is not selected for tracking in the Vicon system. -The second terminal is used to launch the estimator for the drone. If you want to use the default settings, it's enough to specify your drone ID with the `--drone_name` argument. For advanced settings, you need to modify the `estimators.toml` file in the `drone-estimators` repository or pass a path to your TOML file with the `--settings` argument. +The second terminal is used to launch the estimator for the drone. If you want to use the default settings, it's enough to specify your drone ID with `pixi run -e deploy estimator `. You need to use the actual DEC number on the drone or the name shown in rviz. If the estimator works, you should see the frequency information in the terminal. ```bash -pixi shell -e deploy -python -m drone_estimators.ros_nodes.ros2_node --drone_name cf52 +pixi run -e deploy estimator cf01 ``` -## Generating Tracks for Level 3 Deployment +## Deploying Your Controller + +To deploy your controller on the real drone, use the deployment script in the `lsy_drone_racing/scripts` folder. Place the drone on its start position, power it on, and launch the estimators. + +!!! note + Make sure the drone has enough battery to complete the track. If a red LED is constantly turned on, the drone is low on battery. A blinking red LED indicates that the battery is sufficiently charged. -For level 3 deployment, you need to generate new configuration file with a track layout based on the real-world positions of the gates and obstacles. To do so, use the `save_track_as_config.py` script in the `lsy_drone_racing/scripts` folder. +In the third terminal, start the deploy environment with `pixi shell -e deploy` and the deployment script with the correct configuration and controller. ```bash -python scripts/save_track_as_config.py --config --save_config_to +pixi shell -e deploy +python scripts/deploy.py +# or +python scripts/deploy.py --config .toml --controller .py ``` -The script will query the Vicon system for the current positions of all gates and obstacles, and generate a new deploy-ready TOML configuration file with the specified name. +!!! note + Be careful when flying the drone! Make sure to kill the process (**Ctrl+C**) immediately when your controller is unstable. -## Deploying Your Controller +The deployment script will first check if the real track poses and the drone starting pose is within acceptable bounds of the configured track. If not, the script will print an error message and terminate. If the poses are correct, the drone will take off, fly through the track, print out the final lap time, and land automatically. -To deploy your controller on the real drone, use the deployment script in the `lsy_drone_racing/scripts` folder. Place the drone on its start position, power it on, and launch the estimators. +## Saving a Track Layout (Optional) -!!! note - Make sure the drone has enough battery to complete the track. If a red LED is constantly turned on, the drone is low on battery. A blinking red LED indicates that the battery is sufficiently charged. +If you want to save the measured layout for later use, run the `save_track_as_config.py` script in the `lsy_drone_racing/scripts` folder: ```bash -python scripts/deploy.py --config --controller +pixi shell -e deploy +python scripts/save_track_as_config.py --config .toml --save_config_to .toml ``` -The deployment script will first check if the real track poses and the drone starting pose is within acceptable bounds of the configured track. If not, the script will print an error message and terminate. If the poses are correct, the drone will take off, fly through the track, print out the final lap time, and land automatically. +The script saves the measured gate, obstacle, and drone starting poses to `config/.toml`. diff --git a/docs/challenge/simulation.md b/docs/challenge/simulation.md index 256053d02..0ec677a42 100644 --- a/docs/challenge/simulation.md +++ b/docs/challenge/simulation.md @@ -4,7 +4,7 @@ The first part of the challenge is to complete the race track in simulation. To ## Running the Simulation -Once you have installed the `lsy_drone_racing` package, you should be able to start a simulation run with +Once you have installed the `lsy_drone_racing` package, you should be able to start a simulation inside a `pixi shell` with ```bash python scripts/sim.py @@ -18,6 +18,8 @@ python scripts/sim.py -r -n 10 This will render the simulation (`-r` or `--render`) and run 10 races in succession (`-n` or `--n_runs`). For a list of all arguments, use `-h` or `--help`. +By default, rendering follows the `render` setting in the `[sim]` section of the config file. Use `--render=True` or `--render=False` to override it for the current run without modifying the configuration file. + ## Switching Between Configurations You can choose which configuration to use by changing the `--config` command line option. For example, to run the example controller on the hardest scenario, use the following command: diff --git a/docs/getting_started/setup.md b/docs/getting_started/setup.md index 28c3b1e4e..f93d1016d 100644 --- a/docs/getting_started/setup.md +++ b/docs/getting_started/setup.md @@ -2,45 +2,48 @@ This guide will walk you through the process of setting up the LSY Autonomous Drone Racing project on your system. -## Prerequisites +## Before You Start -Before you begin, ensure you have the following: +### Supported platforms and environments -- Git installed on your system -- A GitHub account -- A [Robostack](https://robostack.github.io/index.html/) environment with [pixi running ROS2 Jazzy](https://robostack.github.io/GettingStarted.html#__tabbed_1_3/). -- Optional: [Docker](https://docs.docker.com/) installed on your system +We recommend using **Ubuntu (x86-64)**, which supports all environments listed below, including GPU simulation and real-drone deployment. -!!! note - You can also use [micromamba](https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html) or other dependency management tools, but we won't cover them here in full detail. +| Pixi environment | Purpose | Ubuntu (x86-64) | macOS (Apple Silicon) | Windows (x86-64, native) | +| --- | --- | --- | --- | --- | +| `default` | CPU simulation and controller development | ✅ | ✅ | ✅ | +| `deploy` | Real-drone deployment with ROS 2 | ✅ | ❌ | ❌ | +| `gpu` | CUDA simulation | ✅ | ❌ | ❌ | -## Required Repositories +Use `default` for simulation and `deploy` for real-drone deployment. Follow the installation steps below to set up the appropriate environment. -The LSY Autonomous Drone Racing project requires you to fork the drone racing repository: +### Prerequisites -[lsy_drone_racing](https://github.com/learnsyslab/lsy_drone_racing) (main branch) +Before you begin, ensure you have the following: -This repository contains the drone simulation, environments, and scripts to simulate and deploy the drones in the racing challenge. +- [Git](https://git-scm.com/install/) installed on your system +- A [GitHub](https://github.com/) account -Depending on if you want to use the simulation only or also deploy on real drones, you need additional dependencies. Note that you don't have to install any of those manually, since our setup scripts will take care of that. +## Quick Start: Simulation -### In Simulation +### Install Pixi -- [lsy_drone_racing](https://github.com/learnsyslab/lsy_drone_racing) – environments and scripts for simulation and deployment -- [crazyflow](https://github.com/learnsyslab/crazyflow) – drone simulator -- [drone-models](https://github.com/learnsyslab/drone-models) – Crazyflie dynamics models -- [drone-controllers](https://github.com/learnsyslab/drone-controllers) – controller implementations +We recommend using [Pixi](https://pixi.sh) to manage dependencies and virtual environments for this project. Pixi creates a dedicated .pixi directory in the project root, which contains the isolated virtual environment. We use Pixi in combination with RoboStack. Robostack lets you install your favorite ROS version independent of your OS. Installed packages are cached globally under ~/.cache/rattler/ to speed up subsequent environment setups across projects. -### On Hardware +Install Pixi: -To run the project on real drones, add: +=== "Linux & macOS" -- [motion_capture_tracking](https://github.com/learnsyslab/motion_capture_tracking) – publishes motion capture data to ROS2 -- [drone-estimators](https://github.com/learnsyslab/drone-estimators) – drone state estimators + ```bash + curl -fsSL https://pixi.sh/install.sh | sh + ``` + +=== "Windows" -## Step-by-Step Installation + ```powershell + powershell -ExecutionPolicy Bypass -c "irm -useb https://pixi.sh/install.ps1 | iex" + ``` -### Fork lsy_drone_racing +### Fork and Clone the Repository Start by forking the [lsy_drone_racing](https://github.com/learnsyslab/lsy_drone_racing) repository for your own group. This serves two purposes: @@ -49,63 +52,26 @@ Start by forking the [lsy_drone_racing](https://github.com/learnsyslab/lsy_drone If you're new to GitHub, refer to the [GitHub documentation on forking](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/fork-a-repo). -### Setting up your environment - -#### Pixi package manager (Recommended) - -We recommend using [Pixi](https://pixi.sh) to manage dependencies and virtual environments for this project. Pixi creates a dedicated .pixi directory in the project root, which contains the isolated virtual environment. We use Pixi in combination with RoboStack. Robostack lets you install your favorite ROS version independent of your OS. Installed packages are cached globally under ~/.cache/rattler/ to speed up subsequent environment setups across projects. - -Install Pixi: - -```bash -curl -fsSL https://pixi.sh/install.sh | sh -``` - -To activate the environment, simply run - -```bash -pixi shell -# or -pixi shell -e -``` - -!!! note - To leave a pixi shell, press **ESC** or **Ctrl+D**. Make sure to leave the shell before you activate another shell. - -On the first invocation, Pixi will automatically resolve and install all required dependencies. - -!!! note - In the pixi shell, you can still use pip to install any packages you need. - -#### Micromamba package manager (Not recommended) - -You may also use with [micromamba](https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html). We do not recommend this, since Mamba/Conda environments have the tendency to be leaky and share some system-wide packages. In our experience, this will lead to problems, which is why this project is optimized to use with pixi. If you want to use micromamba anyway, we can't guarantee support. - -#### Docker (Not recommended) - -We also provide a dev container for the simulation environment. However, this is not recommended, since it's heavy and only supports software rendering at the moment. If you're using Windows, make sure to install [WSL](https://docs.microsoft.com/en-us/windows/wsl/install) and [Docker Desktop](https://docs.docker.com/desktop/windows/install/) with WSL integration. Refer to the `Using Docker` section below for more details. - -#### Simulation & Hardware on our Lab PC (If Necessary) - -We provide a Workstation in the Lab on which you are allowed to run your controllers during deployment. Please create a new user for each team and follow the instructions below. - -#### Simulation only (Not Recommended) - -If you only want to run the simulation, you can use your favorite conda/mamba/venv to install the packages. However, you will need a working installation of ROS2 if you want to deploy your controller on the real drone. +First, clone your fork from your own account and create a new environment by running -### Installation +=== "Linux & macOS" -#### Clone repository + ```bash + mkdir -p ~/repos && cd repos + git clone https://github.com//lsy_drone_racing.git + cd lsy_drone_racing + ``` -First, clone your fork from your own account and create a new environment by running +=== "Windows" -```bash -mkdir -p ~/repos && cd repos -git clone https://github.com//lsy_drone_racing.git -cd lsy_drone_racing -``` + ```powershell + mkdir "$HOME\repos" -Force + cd "$HOME\repos" + git clone https://github.com//lsy_drone_racing.git + cd lsy_drone_racing + ``` -#### Install simulation environment (developing & testing controllers) +### Create the Simulation Environment Stay in the repository and run the following command to activate your pixi shell with the default (sim) environment: @@ -113,48 +79,35 @@ Stay in the repository and run the following command to activate your pixi shell pixi shell ``` -!!! note - Some subpackages currently depend on a prerelease version of [scipy](https://github.com/scipy/scipy), which needs to be built from source. This might take more than 10 minutes on older hardware. - !!! note By running the commands above, our automated scripts will install and activate **acados** by default. This might cause the terminal to freeze for several minutes. [Acados](https://docs.acados.org/index.html) is an Optimal Control Framework that can be used to control the quadrotor using a Model Predictive Controller. If something does not work out of the box, we refer the reader to the [official installation guide](https://docs.acados.org/installation/). -To speed up simulation with GPU (optional), run: +On the first invocation, Pixi will automatically resolve and install all required dependencies. -```bash -pixi shell -e gpu -``` +!!! note + To leave a pixi shell, run `exit` or press **Ctrl+D**. Make sure to leave the shell before you activate another shell. + +### Run Your First Simulation Finally, you can test if the installation was successful by running ```bash -cd ~/repos/lsy_drone_racing -python scripts/sim.py -r +python scripts/sim.py ``` If everything is installed correctly, this opens the simulator and simulates a drone flying through four gates. -(Optional) If you want to train RL policies, we recommend using a GPU-enabled environment for optimal performance. To install additional dependencies including [PyTorch](https://pytorch.org/) and [Wandb](https://wandb.ai/), stay in the gpu shell and run: - -```bash -pip install -e .[rl] -``` +## Hardware Setup (Ubuntu Only) -(Optional) You can also run the tests by directly running either +### Prerequisites -```bash -pixi run -e tests tests -v -``` +- A [Robostack](https://robostack.github.io/index.html/) environment with [pixi running ROS2 Kilted](https://robostack.github.io/GettingStarted.html#__tabbed_1_3/)（automatically installed during the deployment setup below） -or by first activating the correct environment +### Simulation & Hardware on our Lab PC -```bash -pixi shell -e tests -cd ~/repos/lsy_drone_racing -pytest tests -``` +We provide a Workstation in the Lab on which you are allowed to run your controllers during deployment. Please create a new user for each team and follow the instructions below. -#### Install deployment environment (deploy controller to real drones) +### Create the Deployment Environment This is for the deployment in the lab, either on your own machine or on the lab PC. With a fresh terminal, stay in the repository and run: @@ -167,11 +120,7 @@ This will automatically create a ros2 workspace with RoboStack, clone the motion !!! note By running the commands above, our automated scripts will install and activate **acados** by default. This might cause the terminal to freeze for several minutes. [Acados installation guide](https://docs.acados.org/index.html) is an Optimal Control Framework that can be used to control the quadrotor using a Model Predictive Controller. If something does not work out of the box, we refer the reader to the [official installation guide](https://docs.acados.org/installation/). -Test your installation: For this to work you have to be in the lab and be connected to our local network. - -```bash -ping 10.157.163.191 -``` +### Configure Crazyradio Permissions If this works, you need a total of *three* open terminals with the deploy environment activated to actually deploy your controller. Before we do that, however, we need to prepare the USB port for the Crazyradio to send commands to the Crazyflie drones. For that, execute the following block. Ask the TA to help you with sudo rights. @@ -198,32 +147,75 @@ sudo udevadm trigger For TAs: Switch to the admin account using `su` and replace `YOUR-USERNAME` with the student's username. -Now you are ready to deploy your controller on real drones. First, run the motion capture tracking node. If there are valid elements in the motion capture area, you should be able to see them in the rviz window. +For motion tracking, state estimation, and controller deployment, continue with [Deployment](../challenge/deployment.md). + +## Optional Workflows + +### GPU Simulation and RL Training (Ubuntu Only) + +(Optional, Ubuntu Only)To speed up simulation with GPU , run: ```bash -ros2 launch motion_capture_tracking launch.py +pixi shell -e gpu ``` -Second, start another deploy shell and run the estimator node. Please check the actual DEC number on the drone, or the name shown in rviz. If this works, you should be able to see frequency information in terminal. +(Optional, Ubuntu Only) If you want to train RL policies, we recommend using a GPU-enabled environment for optimal performance. To install additional dependencies including [PyTorch](https://pytorch.org/) and [Wandb](https://wandb.ai/), stay in the gpu shell and run: ```bash -python -m drone_estimators.ros_nodes.ros2_node --drone_name cf10 +pip install -e .[rl] ``` -Lastly, run the deployment script with the correct configuration and controller. +### Running Tests + +(Optional) You can also run the tests by directly running either ```bash -python scripts/deploy.py --config level2.toml --controller +pixi run -e tests tests -v ``` -!!! note - Be careful when flying the drone! Make sure to kill the process (**Ctrl+C**) immediately when your controller is unstable. +or by first activating the correct environment -### Development +```bash +cd ~/repos/lsy_drone_racing +pixi shell -e tests +pytest tests +``` -#### Work on Existing Dependencies +### Building Documentation -If you want to do more in-depth development or understand the used packages ([crazyflow](https://github.com/learnsyslab/crazyflow), [drone-models](https://github.com/learnsyslab/drone-models), [drone-controllers](https://github.com/learnsyslab/drone-controllers), [drone-estimators](https://github.com/learnsyslab/drone-estimators)) better, you can fork and install all of those packages separately in editable mode. If you find bugs or other have improvements, feel free to [submit a pull request](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request) or [create an issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/creating-an-issue) to help us improve the code. The installation procedure is the same for all packages: +```bash +pixi run -e docs docs-build +``` + +To preview the documentation locally: + +```bash +pixi run -e docs docs-serve +``` + +## Advanced Development (Optional) + +This section covers modifying existing dependencies and adding packages for your controllers. You can skip it when getting started with the provided controllers. + +### Dependency Overview + +The repositories below provide the components used by this project. Apart from the main `lsy_drone_racing` repository, the required dependencies are installed automatically during setup. You only need to clone a dependency separately if you want to modify its source code. + +#### In Simulation + +- [lsy_drone_racing](https://github.com/learnsyslab/lsy_drone_racing) – environments and scripts for simulation and deployment +- [crazyflow](https://github.com/learnsyslab/crazyflow) – drone simulator with built-in drone models + +#### On Hardware + +Hardware deployment additionally uses: + +- [motion_capture_tracking](https://github.com/learnsyslab/motion_capture_tracking) – publishes motion capture data to ROS2 +- [drone-estimators](https://github.com/learnsyslab/drone-estimators) – drone state estimators + +### Work on Existing Dependencies + +If you want to do more in-depth development or understand the used packages ([crazyflow](https://github.com/learnsyslab/crazyflow), [drone-estimators](https://github.com/learnsyslab/drone-estimators)) better, you can fork and install all of those packages separately in editable mode. If you find bugs or other have improvements, feel free to [submit a pull request](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request) or [create an issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/creating-an-issue) to help us improve the code. The installation procedure is the same for all packages: ```bash cd ~/repos/lsy_drone_racing @@ -237,14 +229,14 @@ cd ~/repos/lsy_drone_racing After installation, you can change files in the cloned repository and the changes will directly affect your environment. -#### Extended Dependencies +### Extended Dependencies We want to encourage you to use other libraries to speed up your development process. The easiest way to use another library is to install it with pip inside your pixi shell. !!! warning If your controller depends on additional libraries, which are installed locally with pip, the tests and evaluation on GitHub won't work. -To properly add a package to your project, you can either add it tot the `pyproject.toml` file in the root of the repository, or run the following command while being in the correct pixi environment: +To properly add a package to your project, you can either add it to the `pyproject.toml` file in the root of the repository, or run the following command while being in the correct pixi environment: ```bash pixi add @@ -255,50 +247,33 @@ After that, reopen your environment. This automatically adds the package to the !!! note Changing the `pyproject.toml` will also update the `pixi.lock` file, which pins the exact versions of all packages. Make sure to commit both files to your repository, otherwise the tests on GitHub will fail. -### Windows Subsystem for Linux (WSL2) - -It is also possible to develop the project on Windows using WSL2. Clone this project into the WSL2 file system, and follow the same instructions as for Linux. +### Managing Pixi Environments -However, rendering might not work out of the box. To enable software rendering, set the following environment variable in your WSL2 terminal: +To activate the environment, simply run ```bash -export LIBGL_ALWAYS_INDIRECT=1 -python scripts/sim.py -r +pixi shell +# or +pixi shell -e ``` -### Dev Container (Windows 11 WSL2) - -For Windows, you require WSL2 to run the dev container, which enables a Linux environment within Windows. Follow these steps to set up the dev container in VS Code with WSL2: - -**Installation and Setup** - -1. Follow the [official VS Code Dev Containers installation steps](https://code.visualstudio.com/docs/devcontainers/tutorial) to install VS Code Dev Containers in WSL2 and Docker. - - - Make sure to install Ubuntu 22.04 or above in WSL2. - - If you didn't get prompted to enable WSL integration by Docker during installation, open Docker Desktop settings and manually enable WSL integration. **Important:** There are TWO setting options for this. Make sure to enable BOTH! - -2. Clone this project into the WSL2 file system (e.g., `/home/~`) rather than the Windows file system. You can access the WSL filesystem by opening a WSL2/Ubuntu terminal. Performance is significantly better when working on the WSL file system compared to the Windows file system. - -3. Verify dev container configuration: +!!! note + In the pixi shell, you can still use pip to install any packages you need. - - Check the dev container configuration file `.devcontainer/devcontainer.json`. - - Comment out/Uncomment the necessary settings. +### Alternative Environment Managers -4. Open the project in VS Code: +!!! note + You can also use [micromamba](https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html) or other dependency management tools, but we won't cover them here in full detail. - - Select **File → Open Folder** and navigate to your project directory in WSL. - - VS Code should automatically detect the dev container and prompt you to **Reopen in Container**. If not, see the [official instructions](https://code.visualstudio.com/docs/devcontainers/tutorial#_open-the-folder-in-a-container) on how to open it manually. - - Make sure to have the **Dev Containers** extension and **Container Tools** extension installed. +#### Micromamba package manager (Not recommended) -5. Once the container has opened, initialize the environment by opening a terminal and running: +You may also use with [micromamba](https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html). We do not recommend this, since Mamba/Conda environments have the tendency to be leaky and share some system-wide packages. In our experience, this will lead to problems, which is why this project is optimized to use with pixi. If you want to use micromamba anyway, we can't guarantee support. - ```bash - pixi shell - ``` +#### Other package managers for simulation (Not Recommended) -This activates the Pixi environment with all required dependencies installed. You should now be ready to develop with the dev container! +If you only want to run the simulation, you can use your favorite conda/mamba/venv to install the packages. However, you will need a working installation of ROS2 if you want to deploy your controller on the real drone. -## Common errors +## Troubleshooting ### LIBUSB_ERROR_ACCESS (deployment only) @@ -332,6 +307,28 @@ No plugins found, falling back on no decorations Note that starting the simulation with `-r` from a terminal inside VSCode might cause this warning. This will cause your window to not have any decorations (close, minimize, maximize buttons). You can safely ignore this warning. If you want to get rid of it, start the simulation from a regular terminal outside of VSCode. +### PowerShell script execution is disabled (Windows only) + +If `pixi shell` fails with an error like: + +```text +File ...\Temp\tmp....ps1 cannot be loaded because running scripts is disabled on this system. +``` + +PowerShell's `Restricted` execution policy blocks the temporary script that Pixi uses +to activate the environment. Check the policy and allow local scripts for the current +session, then retry: + +```powershell +Get-ExecutionPolicy +Set-ExecutionPolicy -Scope Process -ExecutionPolicy RemoteSigned -Force +pixi shell +``` + +This change requires no administrator privileges and applies only to the current +PowerShell session and its child processes. It expires when they are closed and does +not permanently change the user or system execution policy. + ## Next Steps Once you have successfully set up the project, you can proceed to explore the simulation environment, develop your racing algorithms, and participate in the online competition. Refer to other sections of the documentation for more information on using the project and developing your racing strategies. diff --git a/lsy_drone_racing/__init__.py b/lsy_drone_racing/__init__.py index 0404d1086..a651f81de 100644 --- a/lsy_drone_racing/__init__.py +++ b/lsy_drone_racing/__init__.py @@ -1,7 +1,15 @@ """LSY drone racing package for the Autonomous Drone Racing class @ TUM.""" +import os +import tempfile +from pathlib import Path + from crazyflow.utils import enable_cache import lsy_drone_racing.envs # noqa: F401, register environments with gymnasium -enable_cache() # Enable persistent caching of jax functions +if os.name == "nt": + # Use a Windows-compatible cache path because the default relies on os.getuid(). + enable_cache(cache_path=Path(tempfile.gettempdir()) / "lsy_drone_racing_jax") +else: + enable_cache() # Enable persistent caching of JAX functions. diff --git a/lsy_drone_racing/control/attitude_mpc.py b/lsy_drone_racing/control/attitude_mpc.py index 93d5f052f..886849b9a 100644 --- a/lsy_drone_racing/control/attitude_mpc.py +++ b/lsy_drone_racing/control/attitude_mpc.py @@ -9,11 +9,13 @@ from __future__ import annotations # Python 3.10 type hints +import os from typing import TYPE_CHECKING import numpy as np import scipy from acados_template import AcadosModel, AcadosOcp, AcadosOcpSolver +from acados_template.builders import ocp_get_default_cmake_builder from crazyflow.drones import load_params as load_hardware_params from crazyflow.dynamics.core import load_params as load_dynamics_params from crazyflow.dynamics.so_rpy import dynamics, symbolic_dynamics_euler @@ -154,12 +156,20 @@ def create_ocp_solver( # set prediction horizon ocp.solver_options.tf = Tf + cmake_builder = None + # On Windows, use CMake with MinGW to build the generated MPC solver DLL. + if os.name == "nt": + cmake_builder = ocp_get_default_cmake_builder() + cmake_builder.generator = "MinGW Makefiles" + cmake_builder.build_dir = "build-mingw" + acados_ocp_solver = AcadosOcpSolver( ocp, json_file="c_generated_code/lsy_example_mpc.json", verbose=verbose, build=True, generate=True, + cmake_builder=cmake_builder, ) return acados_ocp_solver, ocp diff --git a/pixi.lock b/pixi.lock index c6fb9ea02..a1d765d4b 100644 --- a/pixi.lock +++ b/pixi.lock @@ -11,6 +11,10 @@ platforms: - 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glfw-preview ; extra == 'preview' - pypi: https://files.pythonhosted.org/packages/e4/be/5b3cfe508bfab6761414ff944e3366eb13be4fd71efcd69450f89ba39f43/protobuf-7.35.1-cp310-abi3-manylinux2014_x86_64.whl name: protobuf version: 7.35.1 @@ -21594,3 +23235,32 @@ packages: name: jax-cuda12-pjrt version: 0.11.0 sha256: 242df99c90827a2937df1c444983d4076173f9836dc0bc1d20df86960144f35a +- pypi: https://files.pythonhosted.org/packages/ff/7a/36306a503085cbfc79c35a28e441c59dd1d8070d2dd65152345ebf2dfec2/warp_lang-1.17.0-py3-none-win_amd64.whl + name: warp-lang + version: 1.17.0 + sha256: ca35c82242a7553046f09023bbe56750b5c3d9af72625bd1a905a56d3189771d + requires_dist: + - numpy + - nvidia-sphinx-theme ; extra == 'docs' + - sphinx-copybutton ; extra == 'docs' + - pre-commit ; extra == 'docs' + - myst-parser ; extra == 'docs' + - usd-core>=25.5 ; (platform_machine != 'ARM64' and platform_machine != 'aarch64' and platform_machine != 'arm64' and extra == 'benchmark') or (platform_machine == 'ARM64' and sys_platform != 'win32' and extra == 'benchmark') or (platform_machine == 'arm64' and sys_platform != 'win32' and extra == 'benchmark') or (platform_machine == 'aarch64' and sys_platform != 'linux' and sys_platform != 'win32' and extra == 'benchmark') + - usd-exchange>=2.2 ; python_full_version >= '3.10' and python_full_version < '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'benchmark' + - blosc>=1.11.1 ; (platform_machine != 'ARM64' and platform_machine != 'aarch64' and platform_machine != 'arm64' and extra == 'examples') or (platform_machine == 'ARM64' and sys_platform != 'win32' and extra == 'examples') or (platform_machine == 'aarch64' and sys_platform != 'win32' and extra == 'examples') or (platform_machine == 'arm64' and sys_platform != 'win32' and extra == 'examples') + - matplotlib>=3.7.5 ; extra == 'examples' + - pillow>=10.4.0 ; extra == 'examples' + - psutil>=7.1.0 ; extra == 'examples' + - pyglet>=2.1.9 ; extra == 'examples' + - usd-core>=25.5 ; (platform_machine != 'ARM64' and platform_machine != 'aarch64' and platform_machine != 'arm64' and extra == 'examples') or (platform_machine == 'ARM64' and sys_platform != 'win32' and extra == 'examples') or (platform_machine == 'arm64' and sys_platform != 'win32' and extra == 'examples') or (platform_machine == 'aarch64' and sys_platform != 'linux' and sys_platform != 'win32' and extra == 'examples') + - usd-exchange>=2.2 ; python_full_version >= '3.10' and python_full_version < '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'examples' + - warp-lang[examples] ; extra == 'torch-cu12' + - torch>=2.7.0 ; extra == 'torch-cu12' + - warp-lang[examples] ; extra == 'torch-cu13' + - torch>=2.9.0 ; extra == 'torch-cu13' + - warp-lang[examples] ; extra == 'dev' + - warp-lang[docs] ; extra == 'dev' + - nvtx ; (platform_machine != 'ARM64' and platform_machine != 'aarch64' and platform_machine != 'arm64' and extra == 'dev') or (platform_machine == 'ARM64' and sys_platform != 'win32' and extra == 'dev') or (platform_machine == 'aarch64' and sys_platform != 'win32' and extra == 'dev') or (platform_machine == 'arm64' and sys_platform != 'win32' and extra == 'dev') + - coverage[toml] ; extra == 'dev' + - ml-dtypes>=0.5.4 ; extra == 'dev' + requires_python: '>=3.10' diff --git a/pyproject.toml b/pyproject.toml index f30031248..b01641b8c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -62,7 +62,7 @@ markers = ["unit", "integration"] ######################## [tool.pixi.workspace] channels = ["conda-forge"] -platforms = ["linux-64", "osx-arm64"] +platforms = ["linux-64", "osx-arm64", "win-64"] [tool.pixi.dependencies] python = ">=3.11.12,<3.14" @@ -71,18 +71,32 @@ pre-commit = "*" packaging = ">=24" pygments = "*" ruff = "*" + +[tool.pixi.target.unix.dependencies] # Compilers needed for acados compilers = ">=1.9.0, <1.11.0" cmake = "==3.26.0" # Pinned for motion_capture_tracking make = "==4.4.1" # Pinned for motion_capture_tracking openblas = ">=0.3.30,<0.4" +[tool.pixi.target.win-64.dependencies] +cmake = "==3.26.0" +vc14_runtime = "*" +gcc = ">=15,<16" +gxx = ">=15,<16" +binutils = "*" +make = "==4.4.1" + [tool.pixi.pypi-dependencies] lsy_drone_racing = { path = ".", editable = true } -[tool.pixi.feature.sim.activation] +# Platform-specific acados setup. +[tool.pixi.feature.sim.target.unix.activation] scripts = ["tools/setup_acados.sh"] +[tool.pixi.feature.sim.target.win-64.activation] +scripts = ["tools/setup_acados.bat"] + [tool.pixi.feature.gpu] platforms = ["linux-64"] diff --git a/tools/setup_acados.bat b/tools/setup_acados.bat new file mode 100644 index 000000000..a4869e97d --- /dev/null +++ b/tools/setup_acados.bat @@ -0,0 +1,100 @@ +@echo off +rem Native Windows setup +setlocal EnableExtensions DisableDelayedExpansion +if not defined PIXI_PROJECT_ROOT goto :not_in_pixi +if not defined CONDA_PREFIX goto :not_in_pixi + +set "ACADOS_ROOT=%PIXI_PROJECT_ROOT%\acados" +set "ACADOS_BUILD=%ACADOS_ROOT%\build-windows" +set "ACADOS_PYTHON=%CONDA_PREFIX%\python.exe" +set "ACADOS_INTERFACE=%ACADOS_ROOT%\interfaces\acados_template" +set "ACADOS_RENDERER=%ACADOS_ROOT%\bin\t_renderer.exe" +set "ACADOS_RENDERER_URL=https://github.com/acados/tera_renderer/releases/download/v0.2.0/t_renderer-v0.2.0-windows-amd64.exe" + +if not exist "%ACADOS_PYTHON%" ( + echo [Setup Acados] ERROR: Pixi Python not found. 1>&2 + goto :failed +) +for %%T in (git cmake gcc g++ mingw32-make) do ( + where %%T >nul 2>&1 + if errorlevel 1 ( + echo [Setup Acados] ERROR: %%T must be on PATH. 1>&2 + goto :failed + ) +) +"%ACADOS_PYTHON%" -c "import struct, subprocess; assert struct.calcsize('P') == 8, '64-bit Python required'; assert subprocess.check_output(['gcc', '-dumpmachine'], text=True).strip() == 'x86_64-w64-mingw32', 'x86-64 MinGW-w64 GCC required'" +if errorlevel 1 goto :failed + +if exist "%ACADOS_ROOT%" goto :check_checkout +echo [Setup Acados] Cloning v0.5.1... +git clone --branch v0.5.1 https://github.com/acados/acados.git "%ACADOS_ROOT%" +if errorlevel 1 goto :failed + +:check_checkout +rem Never reset or overwrite an existing checkout. +if not exist "%ACADOS_ROOT%\.git" ( + echo [Setup Acados] ERROR: Existing acados directory is not a Git checkout. 1>&2 + goto :failed +) +"%ACADOS_PYTHON%" -c "import subprocess, sys; tag = subprocess.check_output(['git', '-C', sys.argv[1], 'describe', '--tags', '--exact-match', 'HEAD'], text=True).strip(); assert tag == 'v0.5.1', 'Expected acados v0.5.1; existing checkout was left unchanged'" "%ACADOS_ROOT%" +if errorlevel 1 goto :failed + +for %%L in (acados hpipm blasfeo) do if not exist "%ACADOS_ROOT%\bin\%%L.dll" goto :build +goto :install_interface + +:build +echo [Setup Acados] Building Windows shared libraries... +git -C "%ACADOS_ROOT%" submodule update --init --recursive +if errorlevel 1 goto :failed +rem Python needs shared libraries. The example MPC uses HPIPM, not qpOASES. +cmake -S "%ACADOS_ROOT%" -B "%ACADOS_BUILD%" ^ + -G "MinGW Makefiles" -DCMAKE_BUILD_TYPE=Release ^ + "-DACADOS_INSTALL_DIR=%ACADOS_ROOT%" ^ + -DBUILD_SHARED_LIBS=ON -DBLASFEO_TARGET=GENERIC ^ + -DHPIPM_TARGET=GENERIC -DACADOS_WITH_QPOASES=OFF +if errorlevel 1 goto :failed +if not defined NUMBER_OF_PROCESSORS set "NUMBER_OF_PROCESSORS=2" +cmake --build "%ACADOS_BUILD%" --parallel %NUMBER_OF_PROCESSORS% +if errorlevel 1 goto :failed +cmake --install "%ACADOS_BUILD%" +if errorlevel 1 goto :failed +for %%L in (acados hpipm blasfeo) do ( + if not exist "%ACADOS_ROOT%\bin\%%L.dll" ( + echo [Setup Acados] ERROR: %%L.dll was not installed. 1>&2 + goto :failed + ) +) + +:install_interface +"%ACADOS_PYTHON%" -c "import importlib.util, pathlib, sys; s = importlib.util.find_spec('acados_template'); sys.exit(0 if s and pathlib.Path(s.origin).resolve().parent.parent == pathlib.Path(sys.argv[1]).resolve() else 1)" "%ACADOS_INTERFACE%" +if not errorlevel 1 goto :renderer +echo [Setup Acados] Installing the Python interface... +"%ACADOS_PYTHON%" -m pip install -e "%ACADOS_INTERFACE%" +if errorlevel 1 goto :failed + +:renderer +if exist "%ACADOS_RENDERER%" goto :validate +echo [Setup Acados] Downloading Tera renderer... +rem Use Pixi's Python standard library; publish only a complete download. +"%ACADOS_PYTHON%" -c "import pathlib, sys, urllib.request; target = pathlib.Path(sys.argv[2]); tmp = target.with_suffix('.exe.download'); urllib.request.urlretrieve(sys.argv[1], tmp); tmp.replace(target)" "%ACADOS_RENDERER_URL%" "%ACADOS_RENDERER%" +if errorlevel 1 goto :failed + +:validate +set "ACADOS_SOURCE_DIR=%ACADOS_ROOT%" +set "ACADOS_INSTALL_DIR=%ACADOS_ROOT%" +set "TERA_PATH=%ACADOS_RENDERER%" +set "PATH=%ACADOS_ROOT%\bin;%PATH%" +"%ACADOS_PYTHON%" -c "import ctypes, os; import acados_template; ctypes.WinDLL(os.path.join(os.environ['ACADOS_SOURCE_DIR'], 'bin', 'acados.dll'), winmode=0); print('[Setup Acados] Python interface and DLL loading OK.')" +if errorlevel 1 goto :failed +echo [Setup Acados] Ready. Windows MPC controllers must use the CMake builder. + +rem Export only activation variables to the calling shell; discard setup variables. +endlocal & set "ACADOS_SOURCE_DIR=%ACADOS_SOURCE_DIR%" & set "ACADOS_INSTALL_DIR=%ACADOS_INSTALL_DIR%" & set "TERA_PATH=%TERA_PATH%" & set "PATH=%PATH%" +exit /b 0 + +:not_in_pixi +echo [Setup Acados] ERROR: Run inside the repository Pixi environment. 1>&2 +:failed +echo [Setup Acados] Setup failed. See the error above. 1>&2 +endlocal +exit /b 1