Department of Computer Science
CSU Stanislaus
California State University
Spring 2026
My Office: DBH 282
Office Hours: TuTh 2:00 p.m.-3:00 a.m. & W 10:00 a.m. - 11:00 a.m.
ZOOM Meeting ID: 4438930033, Phone:
(209) 667-3169, Email: xliang@csustan.edu
Classroom: DBH 101 / Online
Class Date & Time: TR 12:30PM-1:45PM
Class Website: https://www.cs.csustan.edu/~xliang/Courses2/CS4710-26S
Class Canvas: Use your class Canvas account to submit homework assignments
Class
Modality: Hybrid Online - Synchronous. In-person class meetings will be on
campus at the room, day, and time listed. Online class meetings will be at the
day and time listed. Students must be available at the class times listed in
the Class Schedule and must attend in person on days indicated as such by the
instructor. Students do not have the option to choose in-person or virtual, nor
opt for asynchronous participation.
1.
Introduction
to AI Robotics, Second Edition, by Robin R. Murphy, The MIT Press, 2019
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1. |
Introduction to Autonomous Mobile
Robots, Second Edition, by Roland Siegwart,
IIIah Reza Nourbakhsh, and Davide Scaramuzza, The MIT Press, 2011 |
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2. |
Probabilistic Robotics, by Sebastian Thrun, Wolfram Burgard, and Dieter Fox, The MIT Press, August 2005, ISBN: 9780262201629. |
· 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)
· P1: Markov Location (P1A and P1B)
· P2: Kalman Localization (P2A and P2B)
· P3: Particle Filter (P3A, P3B and P3C)
· P4: Path Planning (P4)
· P5: PID Control (P5)
· P6: SLAM (P6)
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CS4710 Mobile 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. |
Students who successfully complete the course must
be able to
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1.
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Understand issues and concepts in autonomous mobile robotics,
problem statements, and typical applications. |
2.
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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.
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3.
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Understand
and apply robotic sensing and perception, and master sensor performance,
sensor classification, uncertainty representation, and feature extraction.
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4.
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Understand
and apply algorithms and methodologies in the robot path planning,
localization, and map making.
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5.
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Write
software programs to control or simulate mobile robots on selected platforms
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