AA 174A / AA 274A / CS 237A / EE 260A / EE 160A

Principles of Robot Autonomy I

Fall 2022

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).

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).


The class syllabus can be found here.


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


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
11 Final Project Presentation and Demo
12/15 3:30 - 6:30 PM
Follow this link to access the course website for the previous edition of Principles of Robot Autonomy I.