Quick start guide¶
What is xamla_motion?¶
xamla_motion is a python client library to interact with ROSVITA based on our server implementation xamlamoveit. With help of xamla_motion it is possible to directly plan and execute trajetories on simulated or real robots, control gripper or manipulate ROSVITA’s WorldView from python 3.5 or above. Further more xamla_motion defines some standard data types wish are usefull in robotic applications like poses or trajectories.
How to install?¶
xamla_motion is already preinstalled in ROSVITA. Therefore, no additional installation is necessary. If you want to use xamla_motion not in the ROSVITA docker image but from your own machine, please clone the ‘xamla_motion repository <https://github.com/Xamla/xamla_motion>’ into your local ROS catkin repository and build it. Please, make sure that all dependencies in setup.py and package.xml are met.
First steps with ROSVITA and xamla_motion by example¶
Create a ROSVITA project¶
After ROSVITA is started, please login and create a new project. A ‘how to’ for this steps is also available:
Then add a ur5 and a WSG-25 gripper as simulated components to the configuration, compile, start ROS and switch to WorldView (how to).
Getting to know xamla_motion data types¶
xamla_motion data types are following two design decisions. The first decision is that the data types are immutable. Therefore, it is not possible to change a value of a existing data type instance but rather it is neccessary to create a new instance which contains the changes.
The second design decision is that all types heavily depend on numpy and especially represent all tensors with help of numpy ndarrays.
When people start to think of how to solve a specific robotics problem many will start to define their problem in poses the robot has to reach and do specific actions with their tools. Therefore, one of the most important data types xamla_motion defines is the Pose data type.
A pose in xamla_motion is defined as three dimensional translation vector [meter], a quaternion which represents the orientation. For quaternion representation xamla_motion use PyQuaternion. Therefore, the creation of an instance of xamla_motions Pose data type is possible with this to parameters.
>>> from xamla_motion.data_types import Pose >>> import numpy as np >>> from pyquaternion import Quaternion >>> p = [0.502, 0.258, 0.367] >>> q = Quaternion(w=0.304, x=0.527, y=0.687, z=0.396) >>> pose = Pose(p, q) >>> print(pose) Pose: translation.x : 0.502 translation.y : 0.258 translation.z : 0.367 quaternion.w : 0.304 quaternion.x : 0.527 quaternion.y : 0.687 quaternion.z : 0.396 frame_id : world >>> pose.translation array([ 0.502, 0.258, 0.367]) >>> pose.quaternion Quaternion(0.304, 0.527, 0.687, 0.396)
As you can see the pose data type also has a third parameter frame id. This parameter specifics in which coordinate system the pose is defined. The default value is world.
Another common representation of poses in robotics is the representation as a 4x4 matrix in homogenous coordinates. The xamla_motion pose data type can also be created from this representation or vise versa.
>>> from xamla_motion.data_types import Pose >>> import numpy as np >>> pose = Pose.from_transformation_matrix(np.eye(4)) >>> print(pose) Pose: translation.x : 0.0 translation.y : 0.0 translation.z : 0.0 quaternion.w : 1.0 quaternion.x : 0.0 quaternion.y : 0.0 quaternion.z : 0.0 frame_id : world >>> pose.transformation_matrix() array([[ 1., 0., 0., 0.], [ 0., 1., 0., 0.], [ 0., 0., 1., 0.], [ 0., 0., 0., 1.]])
Now we have poses which are represented as xamla_motion pose data types. But why we should represent poses with help of this data types. The answer is, because we will do less errors with help of them. For example we can do simple transformations like translate 0.5 meter in +x and +y direction.
>>> from xamla_motion.data_types import Pose >>> import numpy as np >>> translation = np.asarray([0.5, 0.5, 0.0]) >>> pose = Pose.from_transformation_matrix(np.eye(4)) >>> print(pose) Pose: translation.x : 0.0 translation.y : 0.0 translation.z : 0.0 quaternion.w : 1.0 quaternion.x : 0.0 quaternion.y : 0.0 quaternion.z : 0.0 frame_id : world >>> pose.translate(translation) Pose: translation.x : 0.5 translation.y : 0.5 translation.z : 0.0 quaternion.w : 1.0 quaternion.x : 0.0 quaternion.y : 0.0 quaternion.z : 0.0 frame_id : world
In the future more information about the main data types will be added. But for know take a look into the other chapters to learn following: