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 Courses Description

AE 581: Introduction to Robotics & Autonomous System (3-0-3)

Introduction to the fundamentals of mobile robots, spanning the mechanical, motor, sensory, perceptual, and cognitive layers the field comprises.  Overview of the mechanisms for locomotion, dynamic modeling, forward and inverse dynamics, sensing. Concepts of localization and motion planning control theory, signal analysis, computer vision.

Prerequisites: Graduate Standing, cannot be taken for credit with CISE 480 or AE 449

COE 510: Programming Methods for Robotics (3-0-3)

Introduction to the key elements of robotics programming. Key skills in implementing robotic software for real-time systems. Robot programming methods using ROS (Robot Operating System) including creating ROS service servers, implementing ROS nodes, communication between ROS nodes, ROS data structures. Simulation of robotics systems, such as SLAM using ROS frameworks. Hands-on experience in using ROS for programming ground robots and UAVs.

Prerequisites: Graduate Standing

COE 511: Multi-agent Robotic Networks (3-0-3)

Principles of algebraic graph theory. Ad hoc networks: IoT and sensor network enabling technologies, Machine to Machine (M2M) routing protocols, Network topology, connectivity maintenance, fault-tolerance, and coverage> Autonomous multi-robot systems formations, maintenance, and control. Building a network of robots to achieve a common goal, e.g., cooperative Simultaneous Localization and Mapping (SLAM). Case studies.

Prerequisites: AE 581

COE 512: Sensing and Actuation for Intelligent Robots (3-0-3)

Integration of computational intelligence with advanced sensors and actuators for robotic systems perception. Computer vision, robot perception and place recognition, feature extraction, vision object tracking, and obstacle avoidance. Data acquisition and control, robotic system integration. Basic topics in micro-electro-mechanical systems (MEMS) and smart materials sensing and actuation.

Prerequisites: Graduate Standing

ICS 520: Artificial Intelligence and Machine learning for Robotics (3-0-3)

Application of Artificial Intelligence (AI) and Machine Learning (ML) for robotic systems. Intelligent Agents (IA), blind/uninformed and informed search algorithms for path planning. Relational and associative navigation, behavior coordination, uncertainty, and probabilistic reasoning. knowledge representation methods. Different types of IA architectures (operational, systems, and technical) and layers (behavioral, deliberative, interface) within a canonical operational architecture of an intelligent robot. Logical agents, deductive and practical reasoning agents, reactive and hybrid agents, rational agents, and how to use such techniques for creating autonomous robots/agents. Fundamentals and practical usage of Machine Learning (ML) algorithms, including supervised, unsupervised, reinforcement, and evolutionary learning paradigms for implementing autonomous robots/agents.

Prerequisites: Graduate Standing, cannot be taken for credit with CISE 483

SCE 575: Applied Control for Robotic Systems (3-0-3)

The interplay between control and robotics. Kinematic and dynamic models of robot manipulators, mobile robots, and multi-rotor drones, design intelligent controls for robotic systems and explore modeling analogies between these systems. Learn basic linear/nonlinear, single and multiple input/output closed-loop control, Introduction to stability theories, feedback linearization, and robust control design. Basic system identification techniques and the concept of autopilot design for aircraft and UAVs.

Prerequisites: AE  581, cannot be taken for credit with CISE 481

SCE 576: Path Planning and Navigation for Mobile Robot (3-0-3)

Key concepts, algorithms, and design of robot motion and navigation in the presence of obstacles and static and dynamic environments with uncertainty. Real-time feedback control to track the planned motion, C-space obstacles, grid-based motion planning, randomized sampling-based planners, and virtual potential fields. Motion and force control, flying robot trajectory design, UAV's trajectory.

Prerequisites: AE 581, cannot be taken for credit with CISE 482

SCE 578: Human-Robot Interaction (3-0-3)

Principles, methodologies, and algorithms in Human-Robot-Interaction (HRI) such as HRI design methods, cognition, and visual perception, thinking, and actions, autonomy, shared control, remote presence, robot learning, task planning, attention, priming, trust, acceptance, motion control for HRI. Verbal and nonverbal interaction (i.e. speech recognition, natural language understanding, robot gaze, eye movement, touch, gesture and facial recognition, emotion and intention recognition, etc.). Sources of uncertainty in HRI, and ethical considerations for HRI by bringing together knowledge from robotics, artificial intelligence, human-computer interaction, cognitive psychology, etc. Computational models of social intelligence, physical embodiment, mixed-initiative interaction, multi-modal interfaces, human-robot teamwork, robot learning from humans, aspects of social cognition, and long-term interaction.

Prerequisites: COE 512 and ICS 520

COE 619: Project (0-0-6)

A graduate student will arrange with a faculty member to conduct an industrial research project related to the robotics and autonomous intelligent systems field of the study. Subsequently, the students shall acquire skills and gain experience in developing and running actual industry-based projects. This project culminates in the writing of a technical report, and an oral technical presentation in front of a board of professors and industry experts.

Prerequisites: AE 581, COE510, ICS520, and COE 512


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