Teaching

This hands-on course covers fundamental concepts of industrial robotics, including kinematics, programming, and collaborative robot operations. Students gain practical experience programming a real UR10e cobot using PolyScope X interface under guidance from certified trainers. Through realistic lab scenarios, students develop skills in robot task automation, learn to create and optimize robot programs, and master the unique features of PolyScope X for manufacturing applications. Upon completion, students earn the “Educational Robotics Training - Core” certification. See the UR Academy Brochure for certification details.

Explores the application of Augmented Reality and Virtual Reality technologies in modern manufacturing systems. Topics include AR/VR hardware platforms, spatial computing, human-machine interaction, and industrial use cases such as remote assistance, digital twins, and operator training.

An introductory course on robotics covering robot anatomy, coordinate frames, forward and inverse kinematics, trajectory planning, and basic control. Designed for graduate students with an engineering or computer science background.

A doctoral-level special topics course that extends the collaborative robotics curriculum of INEN 5301 to analytical and research depth. Building on hands-on UR10e operation, the course develops homogeneous transformations and DH forward kinematics, analytical inverse kinematics and the Jacobian, robot dynamics with impedance and force control, closed-loop control derivation and PID tuning, hand-eye calibration for vision-guided manipulation, and optimization-based robot cell design. Students also survey current research in human-robot collaboration, complete a literature review, and carry out an original UR10-based research project written and presented in conference-paper format.

A graduate seminar providing professional development in engineering research, technical writing, and scientific communication. Students present their research progress and engage with invited industry and academic speakers.

An introduction to discrete-event simulation as a modeling and decision-support tool for industrial engineering systems that are too complex for closed-form analytical methods. The course covers the nine-step simulation study methodology, queueing theory fundamentals (Kendall notation, Little’s Law, M/M/1, M/M/c, M/G/1), pseudo-random number generation and random variate generation by inverse transform, hand-computed spreadsheet simulation of a queue, and model building, verification, validation, and statistical output analysis in Arena. Students complete an original simulation project as a capstone deliverable.