Department of Computer Science
CSU Stanislaus
California State University
CS4710-001: Mobile
Robotics
Spring 2023
Instructor: Dr. Xuejun Liang
My Office: DBH 282
Office Hours: MWF 10:00AM-11:00AM (ZOOM Meeting ID 4438930033)
Phone: (209) 667-3169, Email: xliang@csustan.edu
Class Information:
Classroom: Bizzini 114 / Online
Class Date & Time: TR 12:30PM-1:45PM
Class Website: https://www.cs.csustan.edu/~xliang/Courses2/CS4710-23S
Important Notification:
|
1. |
This
class will be Hybrid In-Person & Online. The first class will be
In-person. Then,
the first part of the class will be Online, and the second part of the class
will switch to In-person at some point of time during this semester.
In-person class meetings will be on campus at the
room, day, and time listed. The date to switch to In-person will be announced
in advance in Canvas and by email. |
|
2. |
Online class meetings (exams) will be at the day and time listed (TR 12:30PM-1:45PM). But most of my online lectures are asynchronous. This means you can watch my lecture videos at your convenient times and then there is no online class meeting in this situation. However, you do need to check in Canvas for assignments and submit them on time. |
|
3. |
My lecture videos, assignments, tests, and selected answers will be
posted in Canvas Modules on a weekly basis. |
|
4. |
You will submit all your assignments via the Canvas course website. It
is required to scan your assignment into a PDF file if it is written on
paper. Please make sure the file is clearly readable. Program source code
files will be expected for submitting programming assignments and projects.
You should submit all assignments on time. I will accept the
late assignments for maximum three days (including holidays and
weekends) with the point deduction
20% per day. Please note that you will not be able to submit an assignment
after three days of the due day in Canvas. |
|
5. |
There will be no makeup exams
except in a verified emergency with immediate notification. |
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6. |
We have a Course Questions Forum on the discussions panel of our Canvas course website for asking any course-related questions by simply clicking on the “Reply” button. If you know an answer to a classmate’s question, please jump in and answer by clicking “Reply” on the classmate’s question. I encourage all students to participate in this forum. I believe your participation will have a positive impact on your learning. |
Course Materials
Required
Textbook:
1.
Introduction
to AI Robotics, Second Edition, by Robin R. Murphy, The MIT Press, 2019
Reference Books:
1. Introduction to Autonomous Mobile
Robots, Second Edition, by Roland Siegwart, IIIah Reza Nourbakhsh, and Davide
Scaramuzza, The MIT Press, 2011
2.
Probabilistic
Robotics, by Sebastian Thrun, Wolfram Burgard, and Dieter Fox, The MIT Press, August 2005, ISBN:
9780262201629.
Lecture Slides: Introduction to AI Robotics (Chapters 1-5 and 9-15)
· Ch01: What Are Intelligent Robots (Overview)
· Ch02: Brief History of AI Robotics (Overview)
· Ch03: Automation and Autonomy (Overview)
· Ch04: Software Organization: A (Operational Architectures) and B (System Architectures: 4b-1 and 4b2)
· Ch05: Teleoperation (Overview)
· Ch09: Locomotion (Overview)
· Ch10: Sensing (Overview)
· Ch11: Range Sensing (Overview)
· Ch12: Deliberation 12 (Overview)
· Ch13: Navigation 13 (Overview)
· Ch14: Metric Path Planning: A (Overview) and B (Motion planning)
· Ch15: Localization, Mapping, and Exploration: A (Overview) and B (Terrain)
Lecture Slides: Robotics Programing Assignments and Projects
· P1: Markov Location (P1A and P1B)
· P2: Kalman Localization (P2A and P2B)
· P3: Particle Filter (P3A, P3B and P3C)
· P4: Path Planning (P4)
Course Syllabus and Major Topics
Course
Description
CS4950 Robotics. (3 Hours) Pre-requisites: CS 3100 and MATH 2300. This course will introduce robotics and the key artificial intelligence issues involved in the development of intelligent robots. The course will examine algorithms for the control of autonomous mobile robots and explore issues that include software control architectures, localization, navigation, sensing, planning, and uncertainty. Provides a variety of hands-on robot programming and simulation projects.
Course Outcomes:
Students who successfully complete the course must be able to
1.
Understand
issues and concepts in autonomous mobile robotics, problem statements, and
typical applications.
2.
Understand
and apply mobile robot locomotion with legs and wheels and motion control, and
compute with and apply mobile robot kinematic models and constrains,
maneuverability, and workspace.
3.
Understand
and apply robotic sensing and perception, and master sensor performance, sensor
classification, uncertainty representation, and feature extraction.
4.
Understand
and apply algorithms and methodologies in the robot
path planning, localization, and map making.
5.
Write
software programs to control or simulate mobile robots on selected platforms.