Welcome to the Auki Robotics GitHub repository!
This repository hosts tools and software for robotics systems, focusing on precision mapping, navigation, and automation. The posemesh is designed to enable seamless collaboration between devices, including robots, ensuring efficient and coordinated operation in dynamic environments.
All configuration information is accessible through your Auki Network account.
You will need to add the following details in the config/default.yaml file:
domain_id: The ID of the domain you want the map for.posemesh_account: Your console username (e.g.,user@website.com).posemesh_password: Your console password.map_endpoint:https://dsc.auki.network/spatial/crosssectionraycast_endpoint:https://dsc.auki.network/spatial/raycast
Tip: For security, you can add your robot as a User by assigning it to your organization.
Use the following arguments when running the script:
| Argument | Description |
|---|---|
--config CONFIG |
Path to the YAML config file (if moved). |
--image-format {png,bmp,pgm} |
Image format for saving the map. Options: png (default), bmp, or pgm. |
--resolution RESOLUTION |
Pixels per meter (default: 20, max: 200). Higher values result in more detailed maps. |
python3 retrieve_map.py --image-format pgm --resolution 100
# Note: This will generate a .pgm image at 0.01m per pixel resolution typically used by ROS / ROS2.A raycast is an invisible beam sent out from an origin point. It is then possible in the Domain to detect which digital objects this beam would hit. This is extremely important for linking the digital world with the physical world.
The pose of the origin is passed as an argument to the function get_raycast(pose) where pose is a transformation matrix. The transformation matrix is a combination of the rotation matrix (which defines the orientation) and the translation vector (which defines the position), applied to an object (such as a camera) in 3D space. The response will be a json string with the intersection point (as shown below):
{'message': 'Success', 'data': {'hits': [{'point': {'x': -2.3227962546392202, 'y': 0.675, 'z': 0.7821263074874878}, 'distance': 0.5104325206097033, 'normal': {'x': 0, 'y': 0, 'z': -1}, 'faceIndex': 4, 'name': '1451a9ca-2495-4932-bb5d-c97a4a8de6ce:0'}]}}
A simple example is provided.
The transformation matrix can be constructed as follows:
A quaternion (r_x, r_y, r_z, w) can be converted into a 3x3 rotation matrix as follows:
| ( 1 - 2(r_y^2 + r_z^2) ) | ( 2(r_xr_y - wr_z) ) | ( 2(r_xr_z + wr_y) ) |
| ( 2(r_xr_y + wr_z) ) | ( 1 - 2(r_x^2 + r_z^2) ) | ( 2(r_yr_z - wr_x) ) |
| ( 2(r_xr_z - wr_y) ) | ( 2(r_yr_z + wr_x) ) | ( 1 - 2(r_x^2 + r_y^2) ) |
Where:
r_x,r_y,r_zare the components of the quaternion (representing the axis of rotation).wis the scalar part of the quaternion (representing the cosine of half the rotation angle).
The 4x4 transformation matrix combines both rotation matrix (from above) and translation to transform an object in 3D space. It can be represented as:
| ( R_{11} ) | ( R_{12} ) | ( R_{13} ) | ( p_x ) |
| ( R_{21} ) | ( R_{22} ) | ( R_{23} ) | ( p_y ) |
| ( R_{31} ) | ( R_{32} ) | ( R_{33} ) | ( p_z ) |
| ( 0 ) | ( 0 ) | ( 0 ) | ( 1 ) |
Where:
- ( R_{ij} ) are the elements of the 3x3 rotation matrix, which defines the orientation of the object.
- ( p_x, p_y, p_z ) are the components of the translation vector, which defines the position of the object in 3D space.
- The last row ( [0, 0, 0, 1] ) ensures homogeneous coordinates for matrix multiplication.