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CSCI5551-Fall23

Course Syllabus

Table of contents

  1. About
  2. Modality
  3. Discussion Forum
  4. Topics and Course Structure
  5. Prerequisites
  6. Textbook
  7. Guided Programming Projects
  8. Open-ended Final Project
    1. Use of AI Resources on Projects
  9. Quizzes
    1. Use of AI Resources on Quizzes
  10. Grading Policy
  11. Collaboration Policy

About

The goal of this course is to introduce students to robotics principles, covering key topics such as 3D transformations, robot kinematics, forward and inverse kinematics, path planning, configuration spaces, sampling-based planning, basic motion control algorithms, and state estimation for mobile robots, which includes mapping, localization, and SLAM. Students will gain hands-on experience in programming robots in the Java threejs environment. In later projects, they will have the opportunity to control real-world robots using their virtual counterparts using ROS. There will be a open-ended final project where students can apply their skills acquired throughout the semester to explore new ideas. They will present their projects to a wider audience through poster presentations and demos.

This course builds on and is indebted to materials from -

Modality

This section (002) of the course CSCI5551 is scheduled to be held in person, in Rapson Hall 45 on Mondays and Wednesdays 1:00PM-2:15PM Central Time. The lectures will not be recorded. Students are required to attend lectures in person, as long as health and safety concerns and university regulations do not prevent them from doing so. In any case, it is highly recommended to keep the instructor informed of any circumstances that might be of concern for administering the course.

Note: If you want to access via UNITE you will have to register for section (001) taught by Prof. Nikolaos Papanikolopoulos.

There will be no exams (i.e., midterms or final) in this year’s offering; all evaluation will thus take place in the form of programming projects, quizzes, and final project presentations.

Discussion Forum

The Ed Stem discussion forum is available for discussion of course materials including lectures and projects. Any discussion of quizzes and verbatim code on the Ed Stem forum must be posted privately.

Topics and Course Structure

  • Transformations
  • Forward Kinematics
  • Inverse Kinematics
  • Jacobians
  • Path Planning
  • Motion Control
  • Reactive Controllers
  • Configuration Spaces
  • Sampling-based Planning
  • Potential Fields
  • Mobile Robot State Estimation
  • Robot Programming

Prerequisites

  • Strongly encouraged prerequisites:
    • Linear Algebra, Calculus, and Probability
    • Programming fluency in data structures in a classical programming language is essential.
    • Prior experience with the Python programming language or JavaScript is strongly recommended.

Textbook

There is no required textbook for this course, however optional readings will be suggested from the textbook:

  • [1] (Optional) K. Lynch, F. Park, “Modern Robotics: Mechanics, Planning, and Control”, Cambridge University Press New York, NY, USA, 1st Edition, 2017 (ISBN-13:9781107156302).
  • [2] (Optional) J. Craig, “Introduction to Robotics: Mechanics and Control,” Pearson Prentice Hall, NJ, 4th edition, 2017 (ISBN-13: 9780133489798).
  • [3] (Optional but recommended for ROS) Wyatt Newman. “A Systematic Approach to Learning Robot Programming with ROS”. Chapman & Hall. Print ISBN: 9781315152691, 131515269X, eText ISBN: 9781498777872, 1498777872, Edition: 1st. This is freely available through the UMN libraries to students and staff here - link.

Guided Programming Projects

You will complete 7 guided programming projects over the course of the semester.

Open-ended Final Project

You will have opportunity to explore your ideas in an open-ended setting where you can use the tools you have built over the semester to do a final project. You will have opportunity to present your project to wider audience (students, faculty at the U) via poster presentation. It is still TBD whether this project will be done in a group or individually.

Use of AI Resources on Projects

The project and assignments must be your own original work. However, the use of AI tools such as ChatGPT and GitHub Copilot to assist with portions of the implementation are permitted. Although AI can be a powerful tool for a programmer, their use comes with additional responsibilities:

  • The bar for getting an A in this class is higher than just “getting it working.”
  • If you integrate AI-generated code, you are responsible for understanding how it works. Course staff will have many opportunities to check-in with the students over the course leading to requesting you to walk us through the project structure and code.
  • You should expect that the course staff will ask you questions about how the implementation works, especially for complicated functions, and you should be able to provide an explanation that shows you have a thorough understanding of all code in your project.

You are expected to read the University policy listed here.

Quizzes

Throughout the semester, there will be a total of 24 quizzes administered through gradescope. These quizzes will be posted before lecture sections throughout the semester and be available to take until the beginning of lecture that same day. Quizzes will be released at 7:00AM CT and must be submitted by 1:00PM CT. Each quiz will have a 15 minute time limit. Each quiz will consist of 1 or 2 short questions within the scope of previously covered lectures and graded projects.

Your final grade for the quizzes will be based on the best 20 quizzes out of 24 in case you miss any of the quizzes. No additional quizzes will be provided.

Use of AI Resources on Quizzes

Use of AI tools is not permitted on quizzes and will be considered an academic integrity violation. This includes, but is not limited to, services such as ChatGPT, Claude, Bard, Bing Chat, and GitHub Copilot. At the beginning of each quiz, you will need to complete an honor statement affirming that the answers on the quiz are the result of your own work using only the allowed course materials. You are expected to read the University policy listed here.

Grading Policy

Course grades will be determined according to the following criteria:

  • Project 0: 5%
  • Project 1: 12%
  • Project 2: 12%
  • Project 3: 12%
  • Project 4: 12%

  • Project 5: 12%

  • Final Project: 15%
    • Project proposal slides + presentation: 3%
    • Final project video: 6%
    • Poster presentation (evaluation by judges): 6%
  • 20 Pre-Lecture Quizzes: 20% (1% each)

The grading in this course is on an absolute scale. This means that the performance of others in the class will not affect your grade. Letter grades will be assigned using the following scale:

  • A ≥ 93.0%
  • A- ≥ 90.0% and < 93.0%
  • B+ ≥ 87.0% and < 90.0%
  • B ≥ 83.0% and < 87.0%
  • B- ≥ 80.0% and < 83.0%
  • C+ ≥ 77.0% and < 80.0%
  • C ≥ 73.0% and < 77.0%
  • C- ≥ 70.0% and < 73.0%
  • D+ ≥ 67.0% and < 70.0%
  • D ≥ 60.0% and < 67.0%
  • F < 60.0%

For S/N grading, a satisfactory grade (S) requires a grade of 70.0% or above.

Collaboration Policy

The free flow of discussion and ideas is encouraged. But, everything you turn in must be your own work , and you must note the names of anyone you collaborated with on each problem and cite resources that you used to learn about the problem. If you have any doubts about whether a particular action may be construed as cheating, ask the instructor for clarification before you do it. Cheating in this course will result in a grade of F for course and the University policies will be followed.

No code can be communicated, including verbally. Explicit use of external sources must be clearly cited.