Robotics

Robot Mechanics: An understanding and knowledge of robot mechanics such that students can model, analyze, simulate, and program robot manipulators. Students will be able to use robotics principles to model kinematics, generate trajectories, program robot simulations, and program a robot to perform pick and place operations of stationary and moving objects. They will understand and be able to apply principles of basic robotics, including assigning coordinate systems, performing spatial transformations, using DH parameterization to characterize manipulators, deriving forward kinematics, deriving inverse kinematics, performing trajectory planning, creating and applying Jacobians and forward velocities and accelerations, deriving inverse velocities and accelerations, deriving statics of robots, applying Newton-Euler equations, and deriving manipulator dynamics.

Robot Control: An understanding and knowledge of robot control techniques such that students can model, analyze, and apply basic and advanced robot control techniques to serial manipulators in simulations and real hardware. Graduates will be able to model the dynamics of serial manipulators, including actuators, power amplifiers, transmissions, and sensors, and then use that information to analyze and design joint level and operational space controllers consisting of linear controllers, robust controllers, feedforward controllers, and adaptative controllers for posture regulation, trajectory tracking, force control, and hybrid force/position control. Students will be able to simulate, analyze, and tune these techniques using MATLAB/SIMULINK and a real serial link manipulator.

Robot Perception: An understanding and knowledge of the details of robot perception, particularly in relation to image processing or computer vision, such that students will be able to process sensor data via mathematical transformations, filters, and sampling in order to enhance, segment, and reconstruct data for feature detection, object recognition, 3D reconstruction, and probabilistic models.

Robot Cognition: An understanding and knowledge of the details of robot cognition, particularly in relation to motion planning or artificial intelligence, such that students will be able apply theoretical and algorithmic tools to derive a sequence of actions to achieve a desired goal. Students will develop, program, and analyze the performance of a breadth of machine learning and/or artificial intelligence algorithms to develop an understanding of the capabilities and limitations of such algorithms.

In-depth knowledge of academic topics related to robotics: Students will select courses with guidance of their advisory committee to develop in-depth knowledge related to their robotics research. Such classes typically require students to demonstrated mathematical and scientific understandings of topics through homework and project assignments where they apply their knowledge.

Hands-on experience applying concepts and tools from robotics to a project. This is achieved through coursework and dissertation research for the PhD program since there is no separate project requirement. PhD students will develop new scientific foundations that are justified by through literature reviews, intellectual explanations of the work, and results comparing results of that work to current state of the art in the literature.

In depth exposure to and understanding of modern robotics research topics: Students will experience research presentations related to robotics and will be able to prepare statements summarizing each research presentation, what they found most interesting about the research, and what remaining questions they have about the research.

An understanding and knowledge of ethical issues related to robotics.