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

Prof. Jeannette Bohg

Course Assistants

Aniket Bhatia	Zhengguan(Gary) Dai	Brian Dobkowski
Mason Murray-Cooper	Hao Li	Stephanie Newdick
Alvin Sun

Meeting Times

Lectures meet on Tuesdays and Thursdays from 10:30am to 11:50am in Gates Computer Science, B1.

Students are expected to attend one 2-hour section each week. More details will be released soon.

Prof. Bohg's office hours are on Fridays 1:00pm to 2:00pm in Gates 244 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), Thursdays from 6:00pm to 8:00pm (online), and Fridays from 10:00am to 12:00pm (online).

Syllabus

The class syllabus can be found here.

Links

Canvas -- for course content, recordings, and announcements.
Gradescope -- for homework and project submissions.
Edstem -- for discussions and questions.

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 Lecture 2 Pre-knowledge quiz	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	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 Thursday: HW2 due Friday: 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	Lecture 13	Lecture 13 Notes	Section 6 Slides Section 6 Handout (In-Person) Section 6 Handout (Virtual)
8	Monday: HW4 out Object detection / tracking, 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)
N/A	Thanksgiving Break
9	Multi-sensor perception & sensor fusion I (by Daniel Watzenig) Multi-sensor perception & sensor fusion II (by Daniel Watzenig) Friday: HW4 due	Lecture 16; Demo Slides; Demo Code Lecture 17	Lectures 16/17 Notes	Section 8 Slides Section 8 Handout (In-Person) Section 8 Handout (Virtual)
10	Stereo vision State machines Tuesday: Final project check-in due	Lecture 18; Lecture 15 Lecture 19 (State Machines)	Lecture 18 Notes Lecture 19 Notes	Office hours will be held during sections. They are optional but recommended.
11	Final Project Presentation and Demo 12/15 3:30 - 6:30 PM