Programmatic Prototype

Learners

Problem

College juniors and seniors taking an AI robotics course in the Department of Computer Science and Engineering

Implement a path planning and localization algorithm of your choice from the course material. You will use LiDAR sensor data from the Turtlebot4 to solve a maze in real-time.

Programmatic Prototype

Student Materials

Teacher Materials

In this lesson students will leverage Turtlebot4 to develop prototype algorithms for mapping a maze and finding an optimal path through that maze

• Turtlebot v4
• Maze

• Assessment Plan
• Timing Devices

Standards

Student Inquiry

Student Choice

Directions

Anchor

Task

ISTE Standards: 1.4.c Prototypes1.5.d Algorithmic ThinkingState Standards: AP.3B.2

Presentation

Reflection

Coaching

Collaboration

Timeline

Final

Week 4

Week 2 & 3

Week 1

+info

Present

Document

Implement

Plan

Teacher Materials

Back

Students will be graded on the use of github, their teamwork and code reviews, the final report, the completion of the maze and how long it takes, and peer evaluations.

Programmatic Prototype

In this lesson students will leverage Turtlebot4 to develop prototype algorithms for mapping a maze and finding an optimal path through that maze

Standards

ISTE Standards: 1.4.c Prototypes1.5.d Algorithmic ThinkingState Standards: AP.3B.2

Student Materials

Back

Students will work with the turtlebot v4. https://clearpathrobotics.com/turtlebot-4/

Programmatic Prototype

In this lesson students will leverage Turtlebot4 to develop prototype algorithms for mapping a maze and finding an optimal path through that maze

Standards

ISTE Standards: 1.4.c Prototypes1.5.d Algorithmic ThinkingState Standards: AP.3B.2

Standards

ISTE Standards: 1.4.c Prototypes Students develop, test and refine prototypes as part of a cyclical design process. 1.5.d Algorithmic Thinking Students understand how automation works and use algorithmic thinking to develop a sequence of steps to create and test automated solutions. State Standards: AP.3B.2 Implement an artificial intelligence algorithm to play a game against a human opponent or solve a problem.

Student Inquiry

Students will explore different algorithms and their effectiveness, advantages, disadvantages. They will experience the difficulties of implementation and problem solving, as well as working as a functional team member. Additionally, students will be able to compare their approach to that of their classmates at the conclusion of the project.

Student Choice

Students will choose their own team, the leader of the team, the algorithm they will implement. They will distribute tasks among team members, develop an approach to coding and debugging, and methods of documentation and presention

Directions

Week 1: Form teams, select leaders, choose algorithms, set up a public Github repository, and verify Turtlebot4 functionality. Weeks 2 & 3: Write and refine code collaboratively, simulate using Gazebo, test in the physical maze, maintain a clear Github workflow, and document progress via Google Docs. Week 4: Refine and finalize algorithm, complete extensive tests, finalize and submit the detailed report. Final Day: Public presentation of algorithm and live demonstration of robot maze navigation.

Task

Students will be divded up into teams of four. Each team will select and implement a path planning algorithm (e.g. EKF SLAM, A*, D* Lite, RRT) to enable their Turtlebox v4 (provided) to navigation the maze autonomously

Anchor

Students in this project will understand real-world applications involving AI and robotics. By engaging in the maze navigation task, students will get hands on learning experience in applying path finding algorithms within the real world

Collaboration and Teamwork

Students will form groups of four students, collaboratively assign roles and tasks, conduct code reviews, leverage source control software (github) and trouble shoot implementation problems.

Teacher Coaching and Feedback

The professor will monitor the teams progress in Github and provide overall feedback on the coding process and routinely check in with the group for progress. The professor will also provide guidance on different algorithms they might choose to implement.

Student Reflection

Students will individually and collectively reflect on the assignment, the collaboration, challenges and the iterative improvements they make on the project in both their final written report and the final presentation

Public Presentation

On the last day of class, each team will present their project, their algorithm, the implementation strategy, challenges faced, and improvements implemented. Following this presentation will be a live, timed demonstration of the teams turtlebot navigation through the maze using the teams algorithm.

Week 1

Project Setup and Planning

• Choose a team leader
• Choose an algorithm we have learned about in this course to implement (EKF SLAM, A*, D* Lite, RRT, etc)
• Create a public github repository for your team and share it with the professor
• Get your Turtlebot4 from the professor and confirm all sensors and base functionality

Week 2 & 3

Algorithm Implementation and testing

• Begin writing your algorithm, pushing code to the github each time you work
• Test your code using the Gazebo simulator provided with your Turtlebot4
• Perform code reviews with team members to maintain code quality
• Test your code in the Maze located in the Lab as needed
• Create action items within Github and assign them to team members to track progress
• Write report in google docs for your chosen solution

Week 4

Finalize your Code, Presentation, Report

• Refine your algorithm based on testing in simulation and in the Lab Maze
• Complete multiple test runs to ensure intended robot behavior
• Complete Report
• Complete all action items in github

Final Day

• Introduce your Algorithm to the class
• Have your robot complete the Maze during the Classes Final day
• Record your performance
• Upload your final Report