This course surveys the development of robotic systems for navigating in an environment from an algorithmic perspective. It will cover basic kinematics, configuration space concepts, motion planning, and localization and mapping. It will describe these concepts in the context of the ROS software system, and will present examples relevant to mobile platforms, manipulation, robotics surgery, and human-machine systems.
Credit Hours: 3
Instructor: Simon Leonard
Lectures: Tue, Thu 3h00 – 4h15 (Bloomberg 168) 4h30 – 5h45 (Hodson 315)
E-mail: sleonard at jhu dot edu
Office hours: Tuesday 1h30-2h30, Thursday 6h00-7h00 (Zoom link in syllabus)
Course Assistants office hours:
Wyman 170: 12 desktops with Ubuntu 22.04 and ROS2 Humble.
(Primary book) Principles of Robot Motion (Choset et. al.), MIT Press, 2005
Additional online resources will be made available for the assignments.
Probabilistic Robotics, Thrun, Burgard, Fox, MIT Press, 2006.
Planning Algorithms, Steve Lavalle, Cambridge University Press, 2006. book
This course is intended for advanced undergraduates and first year graduate students. I assume students have a rudimentary understanding of linear algebra, calculus, and are able to program in C++ and Matlab.
Goals and Evaluation
There will be five assignments and two exams. Grading will be 45% on the assignments and 25% for each exam and 5% in class quizzes. All students will need to use real robots by the of the term to earn their final mark.
Assignment due dates:
Assignment 1: Sep 21st 2023
Assignment 2: Oct 5th 2023
Assignment 3: Nov 2nd 2023
Assignment 4: Nov 30th 2023
Assignment 5: Dec 8th 2023
Midterm: Oct 12th 2023
Assignments are due by 23:59 EST on the day it is due. 10% per day of the grade is deducted for late submission. Deadlines are provided way ahead of time so work around them. Procrastinate at your own risk.
Turn in assignments on Canvas. No email.
Piazza is used for asking questions and posting comments. Please consider anonymous public comments/questions instead of private ones.
Week 1: Introduction
Week 2: Rigid body motion and velocity. Review of linear algebra used to describe relative 3D motion and velocity between rigid bodies. Kinematics: Forward/inverse kinematics of kinematics chains and mobile robots and manipulator Jacobian.
- Choset, H., Lynch, K.M., Principles of Robot Motion: Theory, Algorithms, and Implementations (Chapter 3).
- Murray, R.M., Li, Z., Sastry, S.S., A Mathematical Introduction to Robotic Manipulation (Chapter 2).
- Siciliano. B., Khatib, O., Springer Handbook of Robotics (Chapter 1).
Week 3: Hand-eye calibration.
- F. Park, B. Martin Robot Sensor Calibration: Solving AX=XB on the Euclidian Group. IEEE Trans. Rob.Aut. 10(5):717-721, October 1994
Week 4: Potential fi elds. Attractive and repulsive potentials. Implementation of sensor constraints as potentials.
- Choset, H., Lynch, K.M., Principles of Robot Motion: Theory, Algorithms, and Implementations (Chapter 4).
- Mezouar, Y., Chaumette, F., Path Planning for Robust Image-based Control, IEEE Tr. on Robotics 2002.
Week 5: Graph-based path planning. Visibility maps, generalized Voronoi diagram, cell decompositions and Minkowski sum.
- Choset, H., Lynch, K.M., Principles of Robot Motion: Theory, Algorithms, and Implementations (Chapters 5, 6).
- de Berg, M., Cheong, O., van Kreveld, M., Overmars, M., Computational Geometry: Algorithms and Applications (Chapters 6, 7, 13, 15).
Week 6: Sample-based path planning. Probabilistic roadmaps, rapidly-exploring random trees.
- Choset, H., Lynch, K.M., Principles of Robot Motion: Theory, Algorithms, and Implementations (Chapters 7).
- LaValle, S., Planning Algorithms (Chapter 5).
Week 7: Review and midterm.
- Choset, H., Lynch, K.M., Principles of Robot Motion: Theory, Algorithms, and Implementations (Chapters 8).
- Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O., MonoSLAM: Real-Time Single Camera SLAM, PAMI 2007.
- Harris, C., Geometry from visual motion. In Active Vision 1993.
Week 10, 11, 12: Bayesian filtering and multiple hyphotheses. Particle fi lter.
- Choset, H., Lynch, K.M., Principles of Robot Motion: Theory, Algorithms, and Implementations (Chapters 9).
- Thrun, S., Fox, D., Burgard, W., Dellaert, F., Robust Monte Carlo localization for mobile robots, Artifi cial Intelligence 2000.
- Doucet, A., de Freitas, N., Murphy, K., Russel, S., Rao-Blackwellized particle fi ltering for dynamic Bayesian networks, Uncertainty in Artifi cial Intelligence 2000.
Week 13: Review
Honor Code: Above all, you must not misrepresent someone else’s work as your own. You can avoid this in several ways:
The strength of the university depends on academic and personal integrity. In this course, you must be honest and truthful. Ethical violations include cheating on exams, plagiarism, reuse of assignments, improper use of the Internet and electronic devices, unauthorized collaboration, alteration of graded assignments, forgery and falsification, lying, facilitating academic dishonesty, and unfair competition.
In addition, the specific ethics guidelines for this course are:
- No teamwork or collaboration on assignments
- Public or private sharing of code, assignments and exams
Report any violations you witness to the instructor. You may consult the associate dean of student conduct (or designee) by calling the Office of the Dean of Students at 410-516-8208 or via email at [email protected]. For more information, see the Homewood Student Affairs site on academic ethics: https://studentaffairs.jhu.edu/policies-guidelines/undergrad-ethics
Read the Graduate Academic Misconduct Policy:
Examples of academic misconduct:
- Use of material produced by another person without acknowledging its source
- Submission of the same or substantially similar work of another person (e.g., an author, a classmate, etc.)
Intentionally or knowingly aiding another student to commit an academic ethics violation
Allowing another student to copy from one’s own assignment, test, or examination
Making available copies of course materials whose circulation is prohibited (e.g., old assignments, texts or examinations, etc.)
Teamwork for assignments is not allowed. You cannot use work from someone else (plagiarism). You cannot share your assignments to someone else (facilitating academic dishonesty).
- You cannot use any material from a classmate or student who took the course in previous semesters.
- You cannot share any material from this course in future semester.
- You cannot use any online tools (AI-based or otherwise) such as ChatGTP or Copilot.
Any academic misconducts (big or small) will be reported to Graduate Affairs (or Office of the Dean) and will be at least sanctioned by an “F” course grade. No negotiation. I will retroactively sanction plagiarism (i.e. a student plagiarize your assignment in future semesters).