Robotics Engineering Courses

This hands on course covers smart materials and actuation, with an emphasis on electroactive polymer (EAP) based materials and actuators, such as contractile EAPs, dielectric elastomers (DEAs), and ion-polymer metal composites (IPMCs). Piezoelectric materials and shape memory alloys (SMAs) are included in the course, as well as pneumatic actuation. Because smart materials and electroactivity are relatively new fields, the course involves literature reviews. Each team project will involve two different types of smart materials, where at least one smart material is electroactive. For the team projects, the class will be organized into groups, ensuring that each group had a mixture of different disciplines to promote lively discussion. Two papers will be required, one as a literature review and one about aspects of the team project. Much of the theory and applied research is yet to be done with smart materials, so this is a very creative course that implements design into the projects, which can include biomimicry. 

Level: Graduate level, cross-referenced through the physical sciences and engineering, and available to upper level undergraduates.

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.

This course will cover deep learning and its applications to perception in many modalities, focusing on those relevant for robotics (images (RGB and RGB-D), videos, and audio). Deep learning is a sub-field of machine learning that deals with learning hierarchical features representations in a data-driven manner, representing the input data in increasing levels of abstraction. 

The course will cover the fundamental theory behind these techniques, with topics ranging from sparse coding/filtering, autoencoders, convolutional neural networks, deep belief nets, and Deep reinforcement networks. We will cover both supervised and unsupervised variants of these algorithms, and we will work with real-world examples in perception-related tasks, including robot perception (object recognition/classification, activity recognition, loop closure, etc.), robot behavior (obstacle avoidance, grasping, navigation, etc.), and more.

The course will involve a project where students will be able to take relevant research problems in their particular field, apply the techniques and principles learned in the course to develop an approach, and implement it to investigate how these techniques are applicable.

Foundations and principles of parallel and walking mechanisms. Topics include advanced spatial/3D kinematics and dynamics of parallel manipulators and legged/walking mechanisms including workspace analysis, inverse and forward kinematics and dynamics, gait analysis of walking mechanisms, motion analysis of parallel mechanisms as well as legged and walking mechanisms, stability/balance analysis of walking mechanisms, and control of parallel manipulators and walking mechanisms. The course will be useful for solving problems dealing with parallel manipulators as well as multi-legged walking mechanisms including humanoid robots, quadruped robots, hexapod robots and all other types of legged walking mechanisms. A final term project would allow students to apply all this information to design, analyze, and simulate parallel and walking mechanisms. Students taking this course are expected to have a background in kinematics and dynamics.

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.

In recent years, robots have become part of our everyday lives. Leaving the research labs to be part of the common tools of a household, tools such as robotic vacuum cleaners (iRobot Roomba, Kalorik), pool cleaners (Polaris, Maytronics), Lawn mowers (Landroid, LawnBott) and more abound. For navigating safely, these robots need the ability to localize themselves autonomously using their onboard sensors. Potential applications of such systems include the automatic 3D reconstruction, 3D reconstruction of buildings, inspection and simple maintenance tasks, metric exploitation, surveillance of public places as well as in search and rescue systems. In this course, we will dive deep into the current techniques for 3D localization, mapping and navigation that are suitable for robotic applications.

Required prerequisites: RBE 500 - Foundations of Robotics, RBE 501 – Robot Dynamics, RBE 502 – Robot Control