Robotics Engineering Courses

In RBE 595 - Advanced Surgical Robotics, you will learn about the most recent advances in surgical robot technology, and how these advances enable new treatment options for medical conditions that are currently considered inoperable. Topics covered in the course will include: design and control of surgical robotic instruments (with special emphasis on continuum designs), surgeon-robot interaction, supervised surgical robot autonomy, image-guided navigation. In addition to technical lectures, the course involves monthly invited seminars from physicians who provide an overview of select clinical procedures and share their perspective on how robotics can help enhance interventional processes for the benefit of patients.The course culminates in a final team project aimed to develop a grant proposal for a new medical robot. The project typically involves collecting preliminary data and/or working on new designs/simulations to support the scientific rationale of the proposal. The proposal is in the National Institutes of Health (NIH) R21 format.

Mathematical models and the tools of high-level logic (first and second order) have been used to guide specification, development, and verification of software and hardware systems in robot systems and other cyber-physical systems. Because of the high cost of application and complex task specifications, formal methods are introduced for verifying and synthesizing provably correct controllers in robotics. This course provides an exposition to formal methods and their connections with control, optimization, machine learning, and game theory. Topics may include automata theory, temporal logic, abstraction-based control, hybrid systems, probabilistic model checking, deductive verification, game theory and reactive synthesis. Students are expected to propose and complete course projects demonstrating their understanding of the topics.

Prerequisite: Foundations of probability and random variables; Linear algebra; Differential equations; control theory as in ECE 504 or RBE 502; Foundation of computer science (computational models, formal languages, complexity theory) or consent of the instructor.

Space and Planetary Robotics course provides historical overview, addresses state of the art and discusses potential future directions of robotics applied to orbiting and voyaging spacecraft technologies and instrumentation, planetary landers and rovers, service, construction and industrial, autonomous and semi-autonomous, conventional and possibly self-replicating robotic systems within non-Earth based settlements, as well as human augmentation systems in the context of space and planetary exploration.

This course is intended for graduate students and advanced undergraduate students. This is term long course. There is no prerequisite for this course. However, it is recommended that this course is taken in conjunction with either courses within minor in Astrophysics, or courses within minor in Aerospace Engineering, or select courses in Engineering Science and Aerospace Engineering such as: ES2501 Introduction to Static Systems, ES2503 Introduction to Dynamic Systems, AE2713 Astronautics, AE4713 Spacecraft Dynamics and Control, or select courses in Robotics such as RBE1001 Introduction to Robotics, the four course series RBE2001, 2002, 3001 and 3002.

This is a graduate-level course in humanoid robotics: the principles and methods of making human-shaped robots interact with their environment. Topics include: manipulation, perception, locomotion, balance, coordination, control, interfaces, and human-robot interaction. Recommended background is advanced graduate standing in Robotics Engineering. Familiarity with ROS and a recursive programming language is assumed.

This course will cover a wide range of topics in swarm intelligence, including mathematical, computational, and biological aspects. The course is organized in four parts. In the first part, the students will learn about complex systems and the basic concepts of self-organization, such as positive and negative feedback, symmetry breaking, and emergence. The second part concerns several types of network models, such as information cascades, epidemics and voting. The instructor will illustrate a diverse collection of self-organized systems in nature, finance, and technology that concretize these concepts. The third part is dedicated to swarm robotics, and will cover common swarm algorithms for task allocation, collective motion, and collective decision-making. The fourth and final part covers optimization algorithms inspired by swarm intelligence, namely ant colony optimization and particle swarm optimization. The course will blend theory and practice, challenging the students to learn by implementing the algorithms discussed in class. The final project will involve working on a research problem in swarm robotics, and the final deliverable will include a demo and a research paper.