README.md

Cartesian Motion Controller

This controller is a flexible and versatile default for Cartesian motion without contact. The primary use case is approaching and tracking moving targets in the robots workspace. One of the advantages is that the controller interpolates for you, i.e. you do not need to densely sample intermediate poses towards your targets.

As an example, the CartesianMotionController can be given sparsely sampled target poses with a relatively low frequency. Depending on their use case, users can obtain anything in between smooth, directed motion, or fast jumps to these target poses. If the desired Cartesian trajectories are sampled more high-frequently, the controller can achieve good tracking for more precise tasks.

The interpolation behavior can be tweaked by the following parameters:

• The p and d gains determine the responsiveness in each individual Cartesian axis. The higher these values, the faster does the robot drive to the given target pose. Proportional gains (p) are normally sufficient and you'll probably not need a d gain for most use cases.
• The error_scale achieves this behavior uniformly for all axes. It post-scales the computed error from the controller gains (by multiplication) and is a little easier to adjust with a single parameter. The idea is to first weigh the different Cartesian axes with the controller gains and adjust the overall responsiveness with this parameter.
• The number of internal iterations per robot control cycle. The higher this value, the more does the controller become an inverse kinematics solver for accurate tracking.

Getting Started

We assume that you have the cartesian_controller_simulation package installed.

1. Start the simulation environment as described here.

2. Now we activate the cartesian_motion_controller and a graphical handle for drag-drop interaction:

   ros2 control switch_controllers --start cartesian_motion_controller motion_control_handle

   This activates an interactive marker for moving the robot's end-effector. Drag the marker by its handles. The robot should smoothly follow your lead.

3. Next, open rqt in a new, sourced terminal to change some of the controller's parameters:

   rqt

   Open the Dynamic Reconfigure plugin under Plugins/Configuration and select the cartesian_motion_controller. Play a little with the parameters (e.g. solver/error_scale and solver/iterations) and observe the changes in RViz using the interactive marker as handle.

This concludes our small tutorial of using the cartesian_motion_controller. The approach of steering the robot with the graphical handle is a good first step when working with real robot hardware. It lets you check if everything runs as expected and allows you to adapt the parameters as desired.

When controlling your robot in special use cases, you would normally write custom scripts that continuously publish the controller's target pose by sampling from desired Cartesian trajectory profiles.

Example Configuration

Below is an example controller_manager.yaml for a controller specific configuration. Also see the simulation config for further information.

controller_manager:
  ros__parameters:
    update_rate: 100  # Hz

    cartesian_motion_controller:
      type: cartesian_motion_controller/CartesianMotionController

    # More controller instances here
    # ...


cartesian_motion_controller:
  ros__parameters:
    end_effector_link: "tool0"
    robot_base_link: "base_link"
    joints:
      - joint1
      - joint2
      - joint3
      - joint4
      - joint5
      - joint6

    # Choose: position or velocity.
    command_interfaces:
      - position
        #- velocity

    solver:
        error_scale: 1.0
        iterations: 10

    pd_gains:
        trans_x: {p: 1.0}
        trans_y: {p: 1.0}
        trans_z: {p: 1.0}
        rot_x: {p: 0.5}
        rot_y: {p: 0.5}
        rot_z: {p: 0.5}


# More controller specifications here
# ...