Course Description

This course will cover basic principles for endowing mobile autonomous robots with planning, perception, and decision-making capabilities. Algorithmic approaches for trajectory optimization; robot motion planning; robot perception, localization, and simultaneous localization and mapping (SLAM); state machines. Extensive use of the Robot Operating System (ROS) for demonstrations and hands-on activities. Prerequisites: CS 106A or equivalent, CME 100 or equivalent (for calculus, linear algebra), and CME 106 or equivalent (for probability theory).

Instructor

Dr. Ed Schmerling

Course Assistants

Somrita Banerjee	Robin Brown	Robert Dyro
Thomas Lew	Stephanie Newdick

Meeting Times

Lectures meet on Tuesdays and Thursdays from 9:45am to 11:15am in NVIDIA Auditorium, Huang Engineering Center.

Students are expected to attend one 2-hour section each week (schedule details listed in the syllabus linked below); a mixture of online and in-person (meeting in the Skilling Lab) sections are offered.

Dr. Schmerling's office hours are on Thursdays 12:45pm to 1:45pm in Durand 217 and by appointment.

CA office hours are on Mondays from 1:00pm to 3:00pm (in-person, Skilling Lab), Tuesdays from 2:00pm to 4:00pm (online), and Thursdays from 6:00pm to 8:00pm (online).

Syllabus

The class syllabus can be found here.

Schedule

Subject to change. We will try to have the lecture slides and notes uploaded before each class period.

Week	Topic	Lecture Slides	Lecture Notes	Sections
1	Course overview, mobile robot kinematics Introduction to the Robot Operating System (ROS) Thursday: HW1 out	Lecture 1 Pre-knowledge quiz (solutions) Lecture 2	Lecture 1 Notes Lecture 2 Notes
2	Trajectory optimization Trajectory tracking & closed loop control	Lecture 3; Code Lecture 4	Lecture 3 Notes Lecture 4 Notes	Section 1 Slides Section 1 Handout
3	Motion planning I: graph search methods Motion planning II: sampling-based methods Tuesday: HW1 due, HW2 out	Lecture 5 Lecture 6; Code	Lecture 5 Notes Lecture 6 Notes	Section 2 Slides Section 2 Handout
4	Robotic sensors & introduction to computer vision Camera models & camera calibration	Lecture 7 Lecture 8	Lecture 7 Notes Lecture 8 Notes	Section 3 Slides Section 3 Handout (In-Person) Section 3 Handout (Virtual)
5	Image processing, feature detection & description Information extraction & classic visual recognition Tuesday: HW2 due, HW3 out	Lecture 9 Lecture 10	Lecture 9 Notes Lecture 10 Notes	Section 4 Slides Section 4 Handout (In-Person) Section 4 Handout (Virtual)
6	Intro to localization & filtering theory Parameteric filtering (KF, EKF, UKF)	Lecture 11 Lecture 12	Lecture 11 Notes Lecture 12 Notes	Section 5 Slides Section 5 Handout (In-Person) Section 5 Handout (Virtual)
7	Tuesday: No lecture (Democracy Day) Nonparameteric filtering (PF) Thursday: Final project released Friday: HW3 due, HW4 out	Lecture 13	Lecture 13 Notes	Section 6 Slides Section 6 Handout (In-Person) Section 6 Handout (Virtual)
8	Markov localization and EKF localization Simultaneous localization and mapping (SLAM)	Lecture 14 Lecture 15	Lecture 14 Notes Lecture 15 Notes	Section 7 Slides Section 7 Handout (In-Person) Section 7 Handout (Virtual)
9	Multi-sensor perception & sensor fusion I (by Daniel Watzenig) Multi-sensor perception & sensor fusion II (by Daniel Watzenig)	Lecture 16; Demo Lecture 17	Lectures 16/17 Notes	Section 8 Slides Section 8 Handout (In-Person) Section 8 Handout (Virtual)
N/A	Thanksgiving Break Sunday: HW4 due
10	Stereo vision State machines Tuesday: Final project check-in due	Lecture 18 Lecture 19	Lecture 18 Notes Lecture 19 Notes