Introduction

The goals of this lab are to gain experience working with PID controllers and the ROS parameter server.

Turtlebot PID

• Log into one of the Turtlebot netbooks and check out your group repository using the "Init Ros Workspace" launcher.
• Unzip the file pid_chase.zip in the src folder of your workspace directory. There should now be two projects in that directory:

  robot@turtlebot-07 ~/cs354_ws/src $ ls
  CMakeLists.txt  labs  pid_chase
  

• Take a minute to read over the file pid_chase.py in the scripts directory, and the file pid_chase.launch in the launch directory. Make sure you understand the contents of those files.
• Connect a Turtlebot to your netbook. For this lab you will need to connect both the Kinect and the robot itself. The Kinect should be attached to the USB port on the right side of the netbook. Launch the Turtlebot using pid_chase.launch. Point the robot toward a wall and run pid_chase.py. The robot should reach the target distance, but the process isn't very pretty.
• Modify pid_chase.py so that it reads the PID gain parameters from the parameter server and uses the PID controller to set the velocity.

• Experiment with different values for your gain parameters until you find settings that work well. You want the Turtlebot to smoothly approach the target distance then stop at the appropriate point without excessive oscillations. The p_error term should be driven to a value that is very close to 0. The pid_chase.py node publishes all three error terms. You can echo them to a terminal:

  rostopic echo /p_error
  

  or you can visualize them using rqt_plot:

  rosrun rqt_plot rqt_plot /p_error/data /i_error/data
  

• Once you are satisfied with your gain parameters, modify pid_chase.launch to set the parameters appropriately. Test the new launch file. Once everything is working, check in your final results with the log message
  "PID LAB FINAL SUBMISSION".

If You Have Extra Time

• One weakness of the current version of the project is that the robot makes very fast velocity changes when the distance changes: for example, when someone steps in front of the depth sensor. This results in jerky motions that are hard on the robot's motors.

  We can force the velocity to change smoothly by taking a weighted average between the previous velocity and the desired velocity:

        new_velocity = α × desired_velocity + (1 - α) × previous_velocity

  Values of α near 1 will lead to fast velocity changes, while values of α near 0 will lead to slow changes in velocity.

• Update pid_chase.py to implement velocity smoothing. (You may need to re-tune your PID gain terms. In effect, smoothing the velocity changes the dynamics of the system that the PID controller is trying to control.)