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ROAS

Robotics and Autonomous Systems

ROAS 5500
Mechatronics Design
[3-0-0:3]
Previous Course Code(s)
ROAS 6000D
Description
This course introduces the essential fundamentals, including modeling, sensing, signal transmission and conversion, actuation, control, simulation, and implementation technologies used within the mechatronics design for robots and autonomous systems. It will give a holistic view of advanced automation technologies in industrial applications and provide the essential skills to design intelligent mechatronics systems. Through this course, students can enhance their understanding of the cross-disciplinary integration and systematic optimization of mechatronics systems involving the knowledge of mechanics, electronics, control engineering, and computer science.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Have an overview of the basic components, functionalities, and applications of mechatronics systems.
- 2.
  Understand the core technologies in modeling, sensing, signal transmission and conversion, actuation, control, and simulation covered by mechatronics design.
- 3.
  Apply the learned knowledge to interpret, analyze and exemplify different areas of mechatronics design, such as legged robots, unmanned aerial vehicles, and autonomous-driving electric vehicles.
- 4.
  Contextualize and analyze mechatronics systems for engineering processes.
- 5.
  Develop a professional engineering sense and adequate maturity to design and implement real-life mechatronic systems.
- 6.
  Cultivate "5C" skills, namely creativity, critical thinking, communication, collaboration, cross-disciplinary capability.
- 7.
  Discover and learn the cutting-edge technologies and theories from the recent research papers and advanced mechatronics products in the related field.
- 8.
  Identify scientific and engineering significances, challenges, and new trends in mechatronics design.
ROAS 5600
Introduction to Discrete Event Systems
[3-0-0:3]
Description
This course aims to provide an introduction to the fundamental knowledge of physical systems modeled with discrete state space and event driven transitions. Discrete Event Systems (DES) arise in the modeling of many engineering domains, such as automated manufacturing systems, communication networks, software systems, process control systems, and transportation systems. This course will introduce a unified modeling framework and emphasize the analysis and control of DES. Basics of automata and language theory are presented first as mathematical preliminaries. Then comes a detailed treatment of state estimation, diagnosis, security and supervisory control theory of DES based on automata model. Topics of other DES models like Petri nets, timed and hybrid automata are also covered towards the end of the course.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Demonstrate mastery of mathematical essentials of discrete event systems.
- 2.
  Have a thorough understanding of the current development of discrete event systems.
- 3.
  Identify the significance of discrete event systems in modern robotics and autonomous systems.
- 4.
  Analyze and design control systems via knowledge of discrete event systems.
- 5.
  Build solid foundations for more advanced topics and courses in control and autonomous systems.
- 6.
  Emerge with a clear picture of both advantages and limitations of theory of discrete event systems.
ROAS 5700
Robot Motion Planning and Control
[3-0-0:3]
Previous Course Code(s)
ROAS 6000C
Description
This course introduces the advanced methodologies in the context of motion planning and control for robotics and autonomous systems. Various methodologies are introduced, including search-based methods, grid-based methods, sampling-based methods, optimization-based methods, learning-based methods, etc. In general, this course covers modern approaches, deep theory, and good practice envisions. In addition to the fundamental knowledge in motion planning and control, the students will also have the opportunity to discover and learn cutting-edge methodologies in the related field, aligning with the substantial developments in robotics, autonomous driving, UAVs, etc.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Have an overview of the basic components, functionalities, and applications of motion planning and control.
- 2.
  Understand the core algorithmic approaches in motion planning and control of robotics and autonomous systems.
- 3.
  Apply learned methodologies to motion planning and control tasks in specific applications (with simulations), such as mobile robots, autonomous driving, UAVs, etc.
- 4.
  Develop presentation skills and scientific writing skills.
- 5.
  Discover and learn cutting-edge methodologies from the recent research papers in the related field.
- 6.
  Identify scientific and engineering significances, challenges, and new trends in motion planning and control.
ROAS 5800
Physical-based Vision for Robot and Autonomous Driving
[3-0-0:3]
Previous Course Code(s)
ROAS 6000B
Description
Light traveling in the 3D world interacts with the scene through intricate processes before being captured by a camera. These processes result in the dazzling effects like color and shading, complex surface and material appearance, different weathering, just to name a few. Physics based vision aims to invert the processes to recover the scene properties, such as shape, reflectance, light distribution, medium properties, etc., from the images by modelling and analyzing the imaging process to extract desired features or information. This course introduces the advanced methodologies in the context of physical-based vision for robotics and autonomous systems. We will introduce diverse techniques, covering from traditional methods based on hand-crafted features to recent deep learning methods. Apart from the fundamental knowledge in physical-based vision, the students will also have opportunities to discover and learn cutting-edge methodologies in popular physical-based vision topics (i.e., bad-weather restoration, shadow detection and removal, specular highlight detection and removal, intrinsic image decomposition, reflection detection and removal, and so on) of the physical-based vision, aligning with the substantial developments in robotics, autonomous driving, UAVs, etc.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Have an overview of the basic components, popular topics, and applications of physical-based vision.
- 2.
  Understand the core algorithmic approaches in physical-based vision for robotics and autonomous systems.
- 3.
  Apply advanced deep learning techniques to address physical-based vision task for robots, autonomous driving, UAVs, etc.
- 4.
  Develop presentation skills and scientific writing skills.
- 5.
  Discover and learn cutting-edge methodologies from the recent research papers in the related field, such as bad-weather restoration, shadow detection and removal, specular highlight detection and removal, intrinsic image decomposition, reflection detection and removal.
- 6.
  Identify scientific and engineering significances, challenges, and new trends in physical-based vision.
- 7.
  Possess research capabilities in computer vision and medical imaging, robots, and autonomous driving.
ROAS 5900
Analytical Methods in Human Factors Research
[3-0-0:3]
Co-list with
INTR 5330
Exclusion(s)
INTR 5330
Background
Previous coursework in Probability and Statistics, including knowledge of estimation, confidence intervals, and hypothesis testing and its use in at least one and two sample problems. Some familiarity with Calculus and Linear Algebra.
Description
The course will cover a wide range of analytical methods used in human factors research domain. The students will gain an understanding of the procedures, objectives and limitations of different research methods. The course will also include four case studies so that students would gain first-hand experience in applying the methods in real projects. These contents are required for research investigating users’ behaviors.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Understand commonly used models of human beings
- 2.
  Acquire techniques in analyzing humans’ behaviors
- 3.
  Understand commonly used statistical models for analyzing experimental data
- 4.
  Gain first-hand experience in designing experiments in human factors area and applying the techniques in research projects
- 5.
  Understand how to present research processes and results (either orally or in writing) in a scientific way
ROAS 5910
Engineering Psychology and Transportation Applications
[3-0-0:3]
Co-list with
INTR 5260
Exclusion(s)
INTR 5260
Description
The course will cover a wide range of engineering psychology topics as well as how the research in these directions can affect policies and regulations in vehicle design and surface transportation. The students will gain an understanding of the characteristics and limitations of human beings from engineering psychology perspectives of view and how the design of traffic control devices, the roadway, the in-vehicle devices, regulations and traffic rules can be affected by the research in these directions.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Understand basic characteristics and limitations of human beings.
- 2.
  Acquire techniques in analyzing road/traffic control design and machine design from human factors point of view.
- 3.
  Understand common collision types on road and corresponding commonly used countermeasures.
- 4.
  Understand characteristics and causes of driver impairments and countermeasures.
- 5.
  Understanding the human factors challenges in the context of driving automation and connected vehicles technologies.
- 6.
  Design and conduct experiments to collect data from human subjects and analyze the data.
- 7.
  Write written reports and orally present the findings in the experiments.
ROAS 6000
Special Topics in Robotics
[3-0-0:3]
Description
Selected topics in robotics of current interest in emerging areas and not covered by existing courses. May be repeated for credit if different topics are covered.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Identify the knowledge of the selected topics in the field of robotics.
ROAS 6800
Seminar in Robotics and Autonomous Systems
[0-1-0:0]
Description
Seminar topics presented by students, faculty and guest speakers. Students are expected to attend regularly and demonstrate proficiency in presentation in accordance with the program requirements. Graded P or F.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Demonstrate effective presentation skills in seminar presentations.
- 2.
  Understand current research trends and applications.
- 3.
  Engage argumentative and critical thinking skills.
ROAS 6900
Independent Study
[1-3 credit(s)]
Description
An independent study on selected topics carried out under the supervision of a faculty member.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Demonstrate mastery of the knowledge and skills in the selected topics related to Robotics and Autonomous Systems.
- 2.
  Apply an interdisciplinary approach in examining the selected topics.
- 3.
  Critically evaluate different aspects of the selected topics.
- 4.
  Communicate findings effectively in written reports.
ROAS 6990
MPhil Thesis Research
Description
Master's thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Design, develop and conduct crossdisciplinary research in Robotics
  and Autonomous Systems.
- 2.
  Communicate research findings effectively in written and oral
  presentations.
ROAS 7990
Doctoral Thesis Research
Description
Original and independent doctoral thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Design, develop and conduct crossdisciplinary research in Robotics and Autonomous Systems.
- 2.
  Communicate research findings effectively in written and oral presentations.

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