About

CS598KKH Advanced Computational Topics in Robotics, Fall 2022

This course introduces students to foundational mathematical models and algorithms used to implement intelligent behavior in autonomous robots, such as autonomous vehicles, drones, industrial robots, and medical robots. Course material will be selected from the following topics:

  • Modeling and representation. 2D/3D transformations, 2D/3D geometry, forward and inverse kinematics, motion representations, configuration space.
  • Motion planning and control. Motion planning, task planning, feedback control, optimal and model predictive control.
  • Perception. Uncertainty modeling, state estimation, visual sensors, 3D mapping, calibration, some computer vision.
  • Software and hardware system integration. Simulation software, visualization and GUIs, distributed system middleware, performance evaluation.

The content of this course will consist of lectures, homework assignments, and simulation-based programming assignments.  Programming will be in the Python language.

This course can be considered as an advanced version of ECE470 / ME445 (Introduction to Robotics) that is intended for graduate students. The breadth of the material is similar, but this course will approach selected topics in greater technical depth and rigor. By the end of this course, students should be better prepared to understand academic papers and implement state-of-the-art methods used in robotics.

For enrolled students, lectures and homework assignments can be found on the official Moodle site at https://learn.illinois.edu.

Time and Location

09:30AM – 10:45AM Mondays and Wednesdays

2101 Everitt Laboratory

Instructor

Office hours: TBD

Prerequisites

Data structures, algorithms, linear algebra, and a second course in calculus (CS 225, CS 374, MATH 241, and MATH 415 or equivalents.) Recommended courses include differential equations, computer graphics, optimization, AI, or ML, but these are not assumed as prerequisites. 

Textbook and readings

Most readings will be in the online Robotic Systems book draft. Some readings will be excerpted from the following online texts:

Coursework

Students must read the assigned readings before class, and must be prepared to discuss the material during class.  Homework / lab assignments will be assigned roughly on a biweekly basis.

Homework must be submitted on the due date electronically at the beginning of class.

Tentative Schedule

  • Week 1-2: Mathematical preliminaries
    Topics: Course introduction, 2D and 3D transformations, coordinate transformations.
    Readings: R.S. Ch. 1-5, Appendix A.1-2
  • Week 3-4: Search and planning
    Topics: Heuristic search, grid-based motion planning, sampling-based motion planning.
    Readings: R.S. Ch. 8-10, Appendix C
  • Week 5-7: Dynamic systems and trajectory optimization
    Topics: Differential equations, underactuated systems, unconstrained and constrained optimization, shooting methods, direct transcription methods, constraints and costs.
    Readings: R.S. Ch. 13, 17.1-4.
  • Week 8: Feedback control
    Topics: PID control, feedforward control, model predictive control, real-time motion planning.
    Readings: R.S. Ch. 15, 17.5.
  • Week 9-10: Probabilistic models
    Topics: Discrete and continuous probability distributions, Bayesian inference, multivariate Gaussian distributions.
    Readings: R.S. Appendix A.3
  • Week 11-13: State estimation
    Topics: Probabilistic filtering, Kalman filter and its variants. Monte Carlo methods and particle filtering. System ID and trajectory prediction.
    Readings: An Introduction to the Kalman Filter, Welch and Bishop, 2006; PML, Ch. 18.
  • Week 14-15: Planning under uncertainty
    Topics: Informative path planning. Markov decision processes, value iteration, policy iteration.
    Readings: R.S. Ch 17

Late assignment policy

Late homework will be accepted with a 10% deduction in grade for each day that the assignment is late. Students with excused absences will receive an extension on the assignment due date.

Grading policy

The final grade will be comprised of homework (60%) participation (10%), and final (30%) grades.  Late homework will be accepted with a 10 point penalty assessed per day late.  The participation grade will be comprised of attendance and in-class discussion.

Final letter grades will be assigned on a curve.

Health and Safety Policies

Following University policy, all students are required to engage in appropriate behavior to protect the health and safety of the community. Students are also required to follow the campus COVID-19 protocols. 

Students who feel ill must not come to class. In addition, students who test positive for COVID-19 or have had an exposure that requires testing and/or quarantine must not attend class. These students are considered to have excused absences for the class period and should contact the instructor via email about making up the work.     

Students who fail to abide by these rules will first be asked to comply; if they refuse, they will be required to leave the classroom immediately. If a student is asked to leave the classroom, the non-compliant student will be judged to have an unexcused absence and reported to the Office for Student Conflict Resolution for disciplinary action. Accumulation of non-compliance complaints against a student may result in dismissal from the University.

Academic Honesty

Students agree to be bound by the UIUC Academic Integrity guidelines.

Homework assignments are expected to be completed individually. Students are permitted and even encouraged to discuss assignments. However, any attempt to duplicate work that is not your own — for example, in the form of detailed written notes, copied code, or seeking answers from online sources — is strictly prohibited and will be considered cheating.