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
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
Follow this link to access the course website for the previous edition of Principles of Robot Autonomy I.