Objective

This assignment is focused on enhancing the control of robots to achieve smoother motion and improved efficiency. You will work on improving a camera-based line-following system, introducing a PI controller, and optimizing the TurtleBot3’s path to a goal with minimal energy consumption. Additionally, you will add a position controller to a robotic arm URDF model.

Tasks

Task 1: Improve Camera-Based Line Following

• Enhance the line-following algorithm from the previous module:
• Improve the speed and smoothness of the line-following behavior by introducing a PI (Proportional-Integral) controller.
• Document the improvement in your robot’s behavior, including the parameters used and their effects.

Task 2: Optimize Goal Selection and Path Planning

• Select a goal for the TurtleBot3 based on the criterion of minimal energy consumption:
• Energy consumption should be evaluated based on the distance to the goal.
• Select the shortest path to the goal and plan the TurtleBot3’s movement accordingly.
• Develop a ROS 2 node that plans and executes the TurtleBot3’s movement to the selected goal with minimal energy consumption.

Task 3: Add a Position Controller to Robotic Arm URDF

• Extend the URDF model of the robotic arm created in the previous module:
• Add a position controller to manage the arm’s movements accurately.
• Ensure the URDF file is correctly configured to simulate the position controller . You need send multiple positions to joints of Robotic Arm.

Task 4: Implement and Visualize LQR for Multi-Goal Following

Objective: Enhance the multi-goal following behavior of TurtleBot3 using LQR and analyze the impact of different Q and R matrix values on the robot’s path and performance.

1. Visualize Goals and Path in RViz:
2. Implement visualizations in RViz to display the positions of the goals and the robot’s path.
3. Add markers for each goal and a trail of points to represent the robot’s odometry during the path following.
4. Experiment with Different Q and R Values:
5. Test the LQR controller with three different sets of Q and R matrices.
6. Record and compare how these values affect the robot’s behavior, particularly in terms of stability, speed, and smoothness when transitioning between goals.
7. Document and Analyze the Results:
8. Provide a detailed report on the performance of the LQR controller with different Q and R settings.
9. Include visualizations from RViz to illustrate the robot’s path and goal positions for each scenario.
10. Discuss which set of Q and R values provided the best balance between responsiveness and smoothness in the robot’s motion.

Submission Process

1. Create Files:
2. Navigate to the module_5_assignment package.
3. Create the required files for the improved line-following, goal selection, and position control tasks.
4. Document Your Work:
5. Create a README.md file in the module_5_assignment package.
6. Provide details about the files you created, including explanations of the code and the commands needed to run your simulations and controllers.
7. Submit Your Assignment:
8. Push your changes to your forked repository.
9. Provide your repository link in the assignment submission text area.
10. Note: Ensure you press the “Start Assignment” button when you see the page (as it takes time to generate the pages).
11. Wait for Review:
12. Wait for the instructors to review your submission.

Learning Outcome

By completing this assignment, you will:

• Learn to control robots to create smoother and more efficient motion.
• Enhance your understanding of using controllers and path planning algorithms to optimize robot behavior.