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OBJECTIVES
STRUCTURE & REQUIREMENTS
Course Description
STUDY PLAN
OBJECTIVES
OBJECTIVES

The PhD in Robotics program aims to produce graduates with the disciplinary preparation and ability to:

  • Synthesize scientific and technical robotics knowledge to identify, formulate and solve research challenges, and effectively disseminate the results to a variety of audiences.
  • Work across multiple disciplines and develop their individual academic, professional and career focus.
  • Keep abreast of the latest advances in science and engineering that contribute to the advancement of knowledge in robotics for the benefit of society.

Program Learning Outcomes

Students graduating with a PhD in Robotics will have the ability to:

  • Demonstrate appropriate breadth and depth of knowledge that is at the frontier of robotics and areas of specialization.
  • Conduct and defend original independent research that results in significant contributions to knowledge in the field of robotics and leads to publishable quality scholarly articles.
  • Understand and value diverse methodologies and techniques for solving critical problems in research.
  • Verify, justify and evaluate the various aspects of the solution to a complex robotics problem.
  • Communicate effectively and professionally, in written and oral forms, the major tenets of the field of robotics and their work to a variety of audiences.
  • Demonstrate a commitment to ethical behavior in research and professional activities.
  • Contribute effectively in teams and collaborative environments.

Completion Requirements

Students admitted to the PhD program with a Master’s Degree must satisfy the following requirements:

  • Complete a minimum of 24 credits of coursework (8 courses of 3 credit hours each). ÌýÌýÌýThis is in addition to two zero-credit PhD Research Seminar courses.  Subject to the advisor’s approval, up to two PhD level courses (maximum of 6 credits) can be taken from other doctoral programs offered at KU.
  • Pass all courses with a minimum passing grade of C in every course.
  • Achieve an overall CGPA (Cumulative Grade Point Average) of at least 3.0 out of 4.
  • Pass the Written Qualifying Exam (WQE), which assesses the technical background of the student. WQE is typically administered at the end of the 2nd regular semester after a full-time student’s admission into the PhD program, and before the end of the 4th semester in the case of a part-time student.
  • Pass the Research Proposal Examination (RPE), typically before the end of the 4th semester for full-time students and before the end of the 6th semester for part-time students.
  • Have at least one full paper accepted for publication in a quartile one ranked journal, per Scopus, in the research field of the dissertation before submitting the request of intent to defend the dissertation. The paper must be based on one of the research contributions in the dissertation, and the student must be the lead author of the paper.
  • Complete a Dissertation on original research and defend it successfully in a viva voce Dissertation Defense examination.

 

Students admitted to the PhD program with only a Bachelor’s Degree must satisfy the following requirements:

  • Complete a minimum of 36 credits of coursework (12 courses of 3 credit hours each).  This is in addition to two zero-credit PhD Research Seminar courses. Subject to the advisor’s approval, up to two PhD level courses (maximum of 6 credits) can be taken from other doctoral programs offered at KU.
  • Pass all courses with a minimum passing grade of C in every course.
  • Achieve an overall CGPA (Cumulative Grade Point Average) of at least 3.0 out of 4. ÌýÌýÌý
  • Pass the Written Qualifying Exam (WQE), which assesses the technical background of the student. WQE is administered after the student successfully completes a minimum of 27 credits of formal coursework.
  • Pass the Research Proposal Examination (RPE), typically before the end of the 6th semester of full-time study.
  • Have at least one full paper accepted for publication in a quartile one ranked journal, per Scopus, in the research field of the dissertation before submitting the request of intent to defend the dissertation. The paper must be based on one of the research contributions in the dissertation, and the student must be the lead author of the paper.
  • Complete a Dissertation on original research and defend it successfully in a viva voce Dissertation Defense examination.
STRUCTURE & REQUIREMENTS
Course Description

ROBO 732 Machine Learning and Applications

(3 Lectures – 3 Credits)

Prerequisites: Advanced data structure, advanced statistics, optimization techniques

Machine learning, a subset of Artificial Intelligence, aims to create systems that automatically improve with experience. It has many applications, including on-line data analysis, data mining and anomaly detection for cyber-security. Prediction and the study of generalization from data are central topics of Data Analysis and Statistics. These two domains aim at the same goal, that is., gaining insight from data and enabling prediction. This course provides a selection of the most important topics from both of these subjects. The course will start with machine learning algorithms, followed by some statistical learning theory, which provides the mathematical foundation for them. We will then bring this theory into context, providing the transition into Bayesian analysis.

ROBO 735 Advanced Computer Vision Paradigms

(3 Lectures – 3 Credits)

Prerequisites: Graduate level course in Image Processing and Analysis

Computer systems that automate the analysis and the interpretation of image are getting increasing demand in areas of basic research and industrial applications. Current applications include remote sensing medical diagnosis from radiographic images, control of manufacturing through parts inspection, image recovery from web servers, database management and image archives, automatic digital photo generation, criminal and forensic investigation, to mention just few. This course covers the essential and recent advanced in computer vision paradigms related deep learning and other advance image analysis techniques for solving real work applications.

ROBO 755 Cognitive Robotics

(3 Lectures – 3 Credits)

Pre requisites: Graduate level courses in Autonomous Robotic Systems and Computer Vision or Image Understanding (or equivalent)

To provide students with an advanced treatment of autonomous systems, how cognitive systems acquire information about the external world through learning and association of interrelationships between the observed world and their contextual frames. To learn how robotics cognitive systems can be designed to produce appropriate responses that make them more intelligent and autonomous.

ROBO 756 Robotic Perception

(3 Lectures – 3 Credits)

Prerequisites: Graduate level courses in Autonomous Robotic Systems and Machine Vision and Image Understanding (or equivalent)

To provide students with knowledge in the principles and practices of quantitative perception for robotic devices. To study both sensing devices and algorithms that emulates perception and intelligent systems. Learn to critically examine the sensing requirements of typical real-

world robotic applications. To acquire competences for development of computational models for autonomous robotic systems.

ROBO 757 Control of Robotic Systems

(3 Lectures – 3 Credits)

Prerequisites: Graduate level knowledge of Engineering Mathematics and Computation (or equivalent).

This course is designed to teach students advanced concepts and tools for analysis, design and control of robotic systems, including advanced concept from nonlinear control and computer vision applied to robots. Complex underactuated systems will be modeled and controlled, including robotic grasping, manipulation and soft robots.

ROBO 764 Optimal Control

(3 Lectures – 3 Credits)

Prerequisites: Graduate level course on Advanced Engineering Mathematics (or equivalent)

This course is designed to teach students methods of optimal control and parameter estimation using Linear Quadratic Gaussian design approach, including optimal control theory of non-deterministic, nonlinear and time-varying systems.

ROBO 794 Selected Topics in Robotics

(3 Lectures – 3 Credits)

Prerequisites: Will be specified according to the particular topics offered under this course number.

This course covers selected contemporary topics in electrical and computer engineering. The topics will vary from semester to semester depending on faculty availability and student interests. Proposed course descriptions are considered by the College or Academic Unit on an ad hoc basis and the course will be offered according to demand. The proposed course content will need to be approved by the Graduate Studies Committee. The Course may be repeated once with change of contents to earn a maximum of 6 credit hours.

STUDY PLAN

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