Robotics Engineering

Multidisciplinary introduction to robotics, involving concepts from the fields of electrical engineering, mechanical engineering and computer science. Topics covered include sensor performance and integration, electric and pneumatic actuators, power transmission, materials and static force analysis, controls and programmable embedded computer systems, system integration and robotic applications. Laboratory sessions consist of hands-on exercises and team projects where students design and build mobile robots. Undergraduate credit may not be earned for both this course and for ES 2201.

This course focuses on mechanical concepts in the design, construction, and actuation of a robot. Topics include the effective conversion of electrical power to mechanical power, power transmission and control for locomotion and payload manipulation, and the application of kinematic principles for the design of planar manipulators. The course will present the physical operation and common robotic applications of different types of actuators, including solenoids, electrical motors, and pneumatic, hydraulic, and soft actuators. The course will address the design of mechanical systems for a robot to meet requirements including chassis strength, durability, reliability, and robustness. Laboratory sessions consist of hands-on exercises and team projects where students design and test mechanical systems for specific tasks. 

This course focuses on how robot control and decision processes are informed through sensors. The course covers the operation and integration of simple and complex sensors, including signal transduction, interface circuitry, and physical integration. Themes include how functionality guides sensor selection; how decision-making is affected by uncertainty, and how performance can be improved through signal conditioning, digital filtering, calibration, parameter selection, and sensor fusion. The course will address how sensor inputs can be used to generate representations of the environment, and how a robot uses information to achieve goals within its environment. Laboratory sessions consist of hands-on exercises and team projects where students specify and test a variety of sensors to accomplish specific tasks. 

This is the third of a four-course sequence introducing foundational theory and practice of Robotics Engineering. The focus of this course is on analysis & control of robotic arms, robotic manipulation, and integration of complex robotic systems, i.e., the coordinated motion of multiple actuators to execute complex manipulation tasks in the physical space. Concepts of transformations along with position and velocity kinematics will be presented, and fundamental concepts of trajectory planning, robot forces and dynamics, computer vision, and control will be introduced. Theoretical methods learned in the classroom will be applied during practical laboratory sessions, which will culminate in the construction and programming of a vision-guided, multi degree of freedom robotic manipulator.

Fourth of a four-course sequence introducing foundational theory and practice of robotics engineering from the fields of computer science, electrical engineering and mechanical engineering. The focus of this course is navigation, position estimation and communications. Concepts of dead reckoning, landmark updates, inertial sensors, and radio location will be explored. Control systems as applied to navigation will be presented. Communication, remote control and remote sensing for mobile robots and tele-robotic systems will be introduced. Wireless communications including wireless networks and typical local and wide area networking protocols will be discussed. Considerations will be discussed regarding operation in difficult environments such as underwater, aerospace, hazardous, etc. Laboratory sessions will be directed towards the solution of an open-ended problem over the course of the entire term.

This course introduces students to the social, moral, ethical, legal, and current or future philosophical issues within the context of robotic systems and related emerging technology. Students will be expected to contribute to classroom presentations, discussions and debates, and to complete a number of significant writing assignments. This course is recommended for juniors and seniors. Students may not receive credit for both RBE 3100 and RBE 31 OX.

This course introduces students to the modeling and analysis of mechatronic systems. Creation of dynamic models and analysis of model response using the bond graph modeling language are emphasized. Lecture topics include energy storage and dissipation elements, transducers, transformers, formulation of equations for dynamic systems, time response of linear systems, and system control through open and closed feedback loops. Computers are used extensively for system modeling, analysis, and control. Hands-on projects will include the reverse engineering and modeling of various physical systems. Physical models may sometimes also be built and tested.

This course focuses on the role of visual sensing in robotic manipulation. It covers fundamental manipulation concepts such as mathematical grasp formulations, grasp taxonomies, and grasp stability metrics. Various grasp planning strategies in the literature are studied. 2D and 3D vision-based control algorithms are covered. Point cloud processing techniques that allow object detection, segmentation, and feature extraction are studied and implemented. Students will integrate all of these aspects to design the whole vision-based robotic manipulation pipeline. 
Students cannot receive credit for both 450X and 4540. 

This is an introductory course on human-robot interaction. It will introduce the behavior and preference of human motor control and motor learning, and how they influence the design of human-robot interface and the dynamics of human-robot interaction. Students will also learn how to conduct human movement studies and social science studies for the design and evaluation of human-robot interfaces. Students in this course will work on interdisciplinary projects, which may involve working with experts in robotics, social science, nursing, and education.

This is an introductory course covering topics in artificial intelligence that are most relevant to robotics applications. Students will learn techniques for perception, planning, and actuation including: (i) informed, uninformed, and adversarial search; (ii) reasoning with uncertainty; (iii) reinforcement learning; and (iv) deep learning. The course will include a series of laboratories culminating in a final project on perception and navigation in a dynamic environment.

Throughout this course, students will be introduced to industrial robots and their applications. The course covers both industrial serial arm robots, such as those equipped with spherical wrist, and industrial parallel manipulators, such as the Stewart-Gough platform and Delta manipulator. Topics include mechanisms’ degrees of freedom, inverse and forward kinematics (position and velocity), workspace, singularity, and manipulability analysis of industrial manipulators. Topics may extend to end effectors, motion accuracy, robot control and automation. This course is a combination of lecture, laboratory and project work. Students will engage in practical, hands-on learning experiences through the use of an industrial robot to apply theoretical knowledge to real-world scenarios, fostering comprehensive mastery of industrial robotics principles. Through the laboratory work, students will become familiar with industrial robotic programming while acquiring skills in working with industrial controllers such as Programmable Logic Controllers (PLC).