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oomwoo-one

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Open-source vacuum cleaner, model One - ROS2 robot description package

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ZIP 다운로드

- - navigation_humble.yaml
  - navigation_iron.yaml
  - navigation_loose.yaml
  - navigation_tight.yaml
- cartographer_lds_2d.lua
- ekf.yaml
- explore_lite.yaml
- gz_bridge.yaml
- kaiaai.yaml
- mapper_params_localization.yaml
- navigation.yaml
- README.md
- self_drive_gazebo.yaml
- telem.yaml
- teleop_keyboard.yaml
- vacuum_bridge.yaml
- sim-bumpers.md
- bringup.launch.py
- living_room.pgm
- living_room.yaml
- bump_map.rviz
- cartographer.rviz
- gazebo.rviz
- inspect_urdf.rviz
- monitor_robot.rviz
- monitor_robot_old.rviz
- navigation.rviz
- sensors.rviz
- test_bumper_wiring.py
- inertial.xacro
- materials.xacro
- params.xacro
- plugins.xacro
- robot.urdf.xacro
.gitignore
CMakeLists.txt
LICENSE
package.xml
README.md

# CDN으로 사용하기

jsDelivr

jsDelivr는 공개 GitHub 리포지토리를 별도 설정 없이 CDN으로 즉시 서빙합니다. 버전과 파일을 고르면 웹페이지에 바로 붙일 수 있는 링크와 예시 코드가 만들어집니다.

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이 문서에서 사용된 명령어를 모아봤습니다. 낯선 명령어가 있다면 펼쳐서 확인해보세요.

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`clock`

설명 보기 ▼

시스템 시계를 설정합니다.
더 많은 정보: <https://www.cisco.com/c/en/us/td/docs/ios/fundamentals/command/reference/cf_book/cf_c1.html#clock>.

clock set {{23}}:{{59}}:{{59}} {{31}} {{april}} {{2000}}

특권 모드에서 실행하여 시스템 시계를 설정:

clock active prefer

링크 반대편과 자동 협상, (기본값: active-clock):

clock passive prefer

링크 반대편과 자동 협상, (기본값: passive-clock):

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<div align="center">

*Open-source robot vacuum you build yourself.*

ROS 2 Jazzy · Gazebo · URDF · Nav2 · SLAM · 2D LiDAR · robot description

![License](https://img.shields.io/badge/license-Apache--2.0-blue)
![Status](https://img.shields.io/badge/status-sim%20ready-brightgreen)
[![Part of OOMWOO](https://img.shields.io/badge/part%20of-OOMWOO-5eead4)](https://github.com/makerspet/oomwoo)

</div>

ROS 2 robot description, Gazebo simulation for **oomwoo-one**, the first
[OOMWOO](https://github.com/makerspet/oomwoo) open-source robot vacuum model. TODO download [3D printing STEP/3MF and CAD design](https://github.com/makerspet/oomwoo-one-cad).

Tutorials:
- [Simulate oomwoo-one in Gazebo with ROS 2](https://makerspet.com/blog/simulate-oomwoo-one-robot-vacuum-in-gazebo-with-ros-2/)
- [Write your first oomwoo ROS 2 package](https://makerspet.com/blog/write-your-first-oomwoo-ros-2-package/)

![Reference robot vacuum cleaner top](https://raw.githubusercontent.com/makerspet/oomwoo/main/assets/vacuum_model_top.webp)

### Video: OOMWOO One Step-by-step Gazebo/ROS2 simulation tutorial
<a href="http://www.youtube.com/watch?feature=player_embedded&v=FOBChivhhkg" target="_blank">
 <img src="http://img.youtube.com/vi/FOBChivhhkg/maxresdefault.jpg" alt="OOMWOO One Step-by-step Gazebo/ROS2 simulation tutorial" width="720" height="405" border="10" />
</a>

## Package contents
- `urdf/` — xacro description of the ~349 mm round vacuum (body + LiDAR turret, diff-drive
  wheels, caster). Frames follow the Kaia.ai convention: `base_footprint → base_link → base_scan`.
- `config/ekf.yaml` — `robot_localization` EKF that fuses `/odom` + `/imu` and publishes the
  `odom → base_footprint` transform (the bridge publishes the `/odom` topic but not this TF,
  which cartographer requires).
- `config/cartographer_lds_2d.lua`, `config/navigation.yaml`, … — SLAM / Nav2 tuning.
- `config/gz_bridge.yaml`, `urdf/plugins.xacro` — Gazebo simulation: diff-drive, odometry
  (ground-truth + wheel), 2D LiDAR, side distance sensors, a front multizone ToF, front
  stereo cameras, and front bumper contact sensors. See
  [Simulation: sensors, topics & tuning](#simulation-sensors-topics--tuning) below, and
  [docs/sim-bumpers.md](docs/sim-bumpers.md) for how the simulated bumpers are wired and
  the three gz-sim gotchas that make them easy to break.
- `launch/bringup.launch.py` — physical bring-up: bridge + `robot_state_publisher` + EKF.

## Usage

Select the robot model (used by the shared Kaia.ai launch files):
```
kaia config robot.model oomwoo_one
```

### Simulation (no robot needed)
```
ros2 launch oomwoo_gazebo world.launch.py
ros2 launch oomwoo_bringup navigation.launch.py use_sim_time:=true slam:=True
ros2 run kaiaai_teleop teleop_keyboard
```

### Physical robot
The robot must be on the LAN running SangamIO (see the [Proscenic root &amp; setup tutorial](https://makerspet.com/blog/tutorial-connect-robot-vacuum-cleaner-to-ros-2-proscenic-m6-pro/) for flashing/Wi-Fi).
```
ros2 launch oomwoo_one bringup.launch.py robot_ip:=<robot-ip>
ros2 launch oomwoo_bringup navigation.launch.py slam:=True
ros2 run kaiaai_teleop teleop_keyboard
ros2 run nav2_map_server map_saver_cli -f ~/maps/map
```
You can store the robot IP once instead of passing `robot_ip:=` every time:
```
kaia config robot.ip <robot-ip>
ros2 launch oomwoo_one bringup.launch.py
```
(Precedence: an explicit `robot_ip:=` wins, otherwise `kaia config robot.ip`, otherwise 192.168.1.143.)

## Simulation: sensors, topics & tuning

Everything below is for the Gazebo sim (`ros2 launch oomwoo_gazebo world.launch.py`). The
gz sensors are defined in `urdf/plugins.xacro` and bridged to ROS 2 by
`config/gz_bridge.yaml`; all sizes/optics are tunable params in `urdf/params.xacro`.

### Sensors

| Sensor | ROS topic(s) | Type | Details | Frame |
|--------|--------------|------|---------|-------|
| 2D LiDAR (turret) | `/scan` | `sensor_msgs/LaserScan` | 360 samples, 5 Hz, 0.1–10 m | `base_scan` |
| Side distance L / R | `/range_left`, `/range_right` | `sensor_msgs/LaserScan` | short-range wall sensors aimed straight out (±90°), ~0.02–0.5 m; a real ToF (`sensor_msgs/Range`) on hardware | `range_left_link`, `range_right_link` |
| Front multizone ToF | `/tof_front/points` | `sensor_msgs/PointCloud2` | 16×8 depth grid, 120°H × 60°V, 0.02–4 m — models two VL53L7CX (each 8×8, 60°) at ±30° | `tof_front_link` |
| Front stereo cameras L / R | `/camera_left/image`, `/camera_right/image` (+ `…/camera_info`) | `sensor_msgs/Image`, `CameraInfo` | RGB, VGA 640×480, 120° HFoV, ~50 mm base (OV5647-equivalent) | `camera_{left,right}_optical_frame` |
| Front bumpers L / R | `/bumper_left/contact`, `/bumper_right/contact` | `ros_gz_interfaces/Contacts` | front 180° contact arc; **non-empty `contacts` = pressed** | `base_link` |
| IMU (gyro + accel) | `/imu` | `sensor_msgs/Imu` | 6-axis, 100 Hz, near body center; angular velocity + linear acceleration + **orientation** (ground-truth attitude; the hardware IMU has none). EKF IMU fusion is off for now | `imu_link` |

> Rendered sensors (LiDAR, side ranges, ToF, cameras) need the sim's GPU render path. On a
> headless/no-GPU setup they advertise but read empty (`inf`/black) — run with a working GL
> stack (or `headless:=true` uses software GL, which is slow and may not render depth).

### Odometry, TF & actuation

| Topic | Type | Notes |
|-------|------|-------|
| `/odom` | `nav_msgs/Odometry` | **canonical** odometry; `odom_source` picks the stream (below) |
| `/odom_truth`, `/odom_wheel` | `nav_msgs/Odometry` | the *other* stream, always published for wheel-slip comparison |
| `/tf`, `/tf_static` | `tf2_msgs/TFMessage` | `odom → base_footprint` (from the selected odom source) + the fixed sensor frames |
| `/joint_states` | `sensor_msgs/JointState` | wheel joints |
| `/clock` | `rosgraph_msgs/Clock` | sim time (use `use_sim_time:=true`) |
| `/cmd_vel` | `geometry_msgs/Twist` | **input** — velocity command to the diff-drive |

**Odometry source switch** — `world.launch.py odom_source:=truth|wheel` selects which odom
owns `/odom` + `/tf`; both streams always publish so you can diff them to measure slip:

| `odom_source` | `/odom` + `/tf` | wheel odom on | ground-truth odom on |
|---------------|-----------------|---------------|----------------------|
| `truth` (default) | ground-truth model pose (slip-free) | `/odom_wheel` | `/odom` |
| `wheel` | wheel-encoder odom (slip drifts) | `/odom` | `/odom_truth` |

### Viewing the sensors

```bash
ros2 topic list                              # everything available
ros2 topic hz /scan                          # confirm a sensor is publishing
ros2 topic echo /bumper_left/contact         # bumpers (non-empty contacts = pressed)
ros2 topic echo /range_right                 # side distance
ros2 run rqt_image_view rqt_image_view       # pick /camera_left/image or /camera_right/image
ros2 run tf2_tools view_frames               # dump the TF tree to frames.pdf
# RViz with the shipped config (LiDAR, ToF PointCloud2, cameras, TF):
ros2 launch oomwoo_bringup monitor_robot.launch.py use_sim_time:=true
#   or: rviz2 -d "$(ros2 pkg prefix oomwoo_one)/share/oomwoo_one/rviz/gazebo.rviz"
```

In RViz, add a **LaserScan** on `/scan` (and `/range_*`), a **PointCloud2** on
`/tof_front/points`, **Image** displays on `/camera_*/image`, and set the fixed frame to
`odom` (or `map` once localized).

### URDF parameters (`urdf/params.xacro`)

All dimensions, sensor placements and optics are xacro properties — edit and re-launch.
Grouped as:

| Group | Example params |
|-------|----------------|
| Body & drivetrain | `base_diameter`, `wheel_diameter`, `wheel_base`, `lower_cylinder_height`, `caster_*` |
| Front bumper | `bumper_facets_per_side`, `bumper_thickness`, `bumper_height`, `bumper_z` |
| Side distance sensors | `range_sensor_{min,max,angle_deg,samples,fov_deg,update_rate,z}` |
| Front ToF | `tof_front_{h_samples,v_samples,hfov_deg,vfov_deg,min,max,update_rate}` |
| Stereo cameras | `camera_{width,height,hfov_deg,baseline,near,far,update_rate}` |
| IMU | `imu_{x,y,z,update_rate,gyro_noise,accel_noise}` |

Related files: `urdf/robot.urdf.xacro` (links/joints), `urdf/plugins.xacro` (gz plugins +
sensors), `urdf/inertial.xacro` (mass/inertia macros), `urdf/materials.xacro` (colors).

### `world.launch.py` arguments (package `oomwoo_gazebo`)

`ros2 launch oomwoo_gazebo world.launch.py <arg>:=<value> …`

| Argument | Default | Values | Meaning |
|----------|---------|--------|---------|
| `robot_model` | *(empty → `kaia config robot.model`)* | package name | robot description package to spawn |
| `world` | `living_room.world` | file in `oomwoo_gazebo/worlds` | Gazebo world |
| `x_pose`, `y_pose` | `-2.0`, `-0.5` | meters | spawn position |
| `use_sim_time` | `true` | `true`/`false` | use the sim `/clock` |
| `headless` | `false` | `true`/`false` | server-only, offscreen, software GL (no GUI) |
| `odom_source` | `truth` | `truth`/`wheel` | which odometry owns `/odom` + `/tf` (see above) |

### Frame tree

```
map → odom → base_footprint → base_link → base_scan            (2D LiDAR)
                                        → wheel_left_link / wheel_right_link / caster_link
                                        → range_left_link / range_right_link
                                        → tof_front_link
                                        → camera_{left,right}_link → camera_{left,right}_optical_frame
```
`map → odom` comes from AMCL (localization); `odom → base_footprint` from the selected odom
source; the rest are fixed frames from `robot_state_publisher`.

## Notes
- URDF dimensions are approximate (~349 mm diameter, ~95 mm height, 0.233 m wheel base to
  match the bridge's odometry). Refine against measurements of your robot.
- Vacuum-specific actuators (vacuum/brushes/water pump, LEDs) are exposed by the bridge via
  `/set_actuator`, `/set_led`, `/set_lidar` and the `/actuator_cmd`, `/led_cmd` topics.

## License
Apache 2.0