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spiri-sdk/README.md

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# Spiri SDK - Simulated robot
The Spiri SDK consists of a number of components. What you're looking at right now
is the drone simulation component, which is the core of the SDK.
Spiri Robots run a number of docker containers to achieve their core functionality,
we try to keep these essential docker containers in one docker compose file. The
docker compose file you'll find in this repository starts an ardupilot-based UAV simulation
as well as a ROS master, and mavproxy to tie it together.
To get started you can simply clone this repository and run `docker compose --profile uav-sim up`.
Once the simulated UAV is running you can connect to it with QGroundControl or other
MavLink compatible software. We expose the UAVs Mavlink conenction on tcp port 5760.
There is experimental GUI support you can enable by running `docker compose --profile uav-sim --profile ui up`.
## Creating a new project
We provide project templates you can use for development that integrate seamlessly into
our simulated robots.
These templates are intended to be used with VSCode.
To get started with our project templates install the [copier](https://copier.readthedocs.io/en/stable/) project
templating utility.
- [template-service-ros1-catkin](https://git.spirirobotics.com/Spiri/template-service-ros1-catkin)
This template uses the last stable release of ROS1 (ros noetic) and supports python and c++ programming
languages.
ROS1 is considered end of life. It's recomended to use a ROS2 template instead
- ROS2 template
We're working on it...
## NVIDIA Container Toolkit
[Source](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
### Installing with Apt
```bash
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
&& curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
```
```bash
sudo apt-get update
```
```bash
sudo apt-get install -y nvidia-container-toolkit
```
#### Configuration
##### Prerequisites
- You installed a supported container engine (Docker, Containerd, CRI-O, Podman).
- You installed the NVIDIA Container Toolkit.
```bash
sudo nvidia-ctk runtime configure --runtime=docker --cdi.enabled
```
```bash
sudo systemctl restart docker
```
##### Rootless Mode
To configure the container runtime for Docker running in [Rootless mode](https://docs.docker.com/engine/security/rootless/), follow these steps:
1. Configure the container runtime by using the nvidia-ctk command:
```bash
nvidia-ctk runtime configure --runtime=docker --config=$HOME/.config/docker/daemon.json
```
2. Restart the Rootless Docker daemon
```bash
systemctl --user restart docker
```
3. Configure /etc/nvidia-container-runtime/config.toml by using the sudo nvidia-ctk command:
```bash
sudo nvidia-ctk config --set nvidia-container-cli.no-cgroups --in-place
```
### Sample Workload
After you install and configure the toolkit and install an NVIDIA GPU Driver, you can verify your installation by running a sample workload.
```bash
sudo docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi
```
Expected output:
```console
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 535.86.10 Driver Version: 535.86.10 CUDA Version: 12.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla T4 On | 00000000:00:1E.0 Off | 0 |
| N/A 34C P8 9W / 70W | 0MiB / 15109MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
```
## Support for Container Device Interface(CDI)
[Source](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/cdi-support.html)
### Prerequisites
- You installed either the NVIDIA Container Toolkit or you installed the `nvidia-container-toolkit-base` package.
The base package includes the container runtime and the `nvidia-ctk` command-line interface, but avoids installing the container runtime hook and transitive dependencies.
The hook and dependencies are not needed on machines that use CDI exclusively
- You installed an NVIDIA GPU Driver.
Two common locations for CDI specifications are `/etc/cdi/` and `/var/run/cdi/`. The contents of the `/var/run/cdi/` directory are cleared on boot.
1. Generate the CDI specification file:
```bash
sudo nvidia-ctk cdi generate --output=/etc/cdi/nvidia.yaml
```
Example output
```console
INFO[0000] Auto-detected mode as "nvml"
INFO[0000] Selecting /dev/nvidia0 as /dev/nvidia0
INFO[0000] Selecting /dev/dri/card1 as /dev/dri/card1
INFO[0000] Selecting /dev/dri/renderD128 as /dev/dri/renderD128
INFO[0000] Using driver version xxx.xxx.xx
...
```
2. (Optional) Check the names of the generated devices:
```bash
nvidia-ctk cdi list
```
Output
```console
INFO[0000] Found 9 CDI devices
nvidia.com/gpu=all
nvidia.com/gpu=0
```
### Sample Workload
```bash
docker run --rm -ti --runtime=nvidia \
-e NVIDIA_VISIBLE_DEVICES=nvidia.com/gpu=all \
ubuntu nvidia-smi -L
```
Output
```console
GPU 0: NVIDIA GeForce RTX 3080 Laptop GPU (UUID: GPU-17c2b9a6-6be2-3857-f8e0-88143e2e621b)
```
## Technologies
* Ubuntu 22.04.1 amd64
* Docker Compose version v2.29.7
* Python 3.10.12
* pip 22.0.2
* NVIDIA Container Toolkit CLI version 1.16.2
* NVIDIA Driver 550.120
* CUDA Version 12.4
## How to Run
Ensure variables in the `.env` file are correct.
Install the required libraries in the python script.
```bash
pip install -r requirements.txt
```
### First Terminal
1. Start the user interface with the following command.
```bash
docker compose --profile ui up
```
2. Click `Launch Gazebo` on the menu.
Simulated world and the drone model should be up and running in a Gazebo instance.
### Second Terminal
This will launch ardupilot, mavproxy and mavros services, and will scale up by the `SIM_DRONE_COUNT` env variable.
1. Start the docker services with the following python script.
```bash
python3 sim_drone.py
```
### QGroundControl
Simulated vehicle(s) should be connected to QGroundControl. `SIM_DRONE_COUNT` value should match the detected vehicle count on GCS.
## Simulation Environment Variables
`DRONE_SYS_ID` and `INSTANCE` environment variables are incremented by 1 for each additional simulated vehicle. `SERIAL0_PORT`, `SITL_PORT`, `MAVROS2_PORT`, `MAVROS1_PORT`,`FDM_PORT_IN` and `GSTREAMER_UDP_PORT` are incremented by 10. Without in-depth knowledge, changing the default value for these ports are not recommended.
| Variable | Type | Default | Description |
| :----------------: | :----: | :--------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------- |
| DRONE_SYS_ID | int | 1 | System-ID for the simulated drone. |
| INSTANCE | int | 0 | Instance of simulator. |
| SERIAL0_PORT | int | 5760 | Mavproxy master port the simulation communicating on. |
| SITL_PORT | int | 5501 | Mavproxy Software in the Loop(SITL) port to send simulated RC input for the simulator. |
| MAVROS2_PORT | int | 14560 | MAVROS ROS 2 UDP port |
| MAVROS1_PORT | int | 14561 | MAVROS ROS 1 UDP port |
| FDM_PORT_IN | int | 9002 | Gazebo Flight Dynamics Model (FDM) UDP port |
| GSTREAMER_UDP_PORT | int | 5600 | UDP Video Streaming port |
| ROS_MASTER_URI | string | "http://0.0.0.0:11311" | This tells ROS 1 nodes where they can locate the master |
| ARDUPILOT_VEHICLE | string | "-v copter -f gazebo-mu --model=JSON -L CitadelHill" | "-v" is vehicle type,"-L" start location, "-f" is vehicle frame type, "--model" overrides simulation model to use |
| WORLD_FILE_NAME | string | "citadel_hill_world.sdf" | Name of the file that exists in the worlds folder. |
| WORLD_NAME | string | "citadel_hill" | Name of the world defined in the world file. |
| DRONE_MODEL | string | "spiri_mu" | Drone model that exists in the models folder. |
| SIM_DRONE_COUNT | int | 1 | Number of drones to be simulated. |
| GCS_PORT | int | 14550 | Ground Control Station(GCS) UDP connection port. |
### Understanding Multi-Vehicle Simulation Parameters
For instance, if `SIM_DRONE_COUNT` is 2, each additional vehicle's ports are incremented by 10.
First simulated vehicle would have these following values,
| Variable | Value |
| :----------------: | :---: |
| DRONE_SYS_ID | 1 |
| INSTANCE | 0 |
| SERIAL0_PORT | 5760 |
| SITL_PORT | 5501 |
| MAVROS2_PORT | 14560 |
| MAVROS1_PORT | 14561 |
| FDM_PORT_IN | 9002 |
| GSTREAMER_UDP_PORT | 5600 |
Second Vehicle,
| Variable | Value |
| :----------------: | :---: |
| DRONE_SYS_ID | 2 |
| INSTANCE | 1 |
| SERIAL0_PORT | 5770 |
| SITL_PORT | 5511 |
| MAVROS2_PORT | 14570 |
| MAVROS1_PORT | 14571 |
| FDM_PORT_IN | 9012 |
| GSTREAMER_UDP_PORT | 5610 |
Video stream would be available on UDP port on 5610 for the second vehicle after enabling streaming.
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