The AI and Software concentration provides focused study in the computing technologies used to develop intelligent robotic systems. Students explore how software, embedded computing, data acquisition and artificial intelligence allow robots to collect information, interpret their surroundings, learn from data and make decisions.

Students choose between pathways in data collection and analysis or machine learning. The data collection and analysis option develops expertise in microcontroller hardware and software, power electronics, computer-based data acquisition, system interfacing, programming, testing and the analysis of information collected from physical systems.

The machine learning option emphasizes object-oriented programming, application development, data structures, software testing, user interfaces and persistent data storage. Students further specialize through coursework in mobile programming or applied machine learning, where they may build machine learning models and apply emerging technologies such as deep learning to real-world problems.

Graduates of this concentration are prepared to develop the software, embedded applications and data-driven capabilities that support intelligent robots and automated systems. Their skills can be applied to robotic perception, monitoring and control, autonomous devices, mobile applications, industrial automation and AI-enabled technology development.

The Autonomy and UXVs concentration provides focused study in the technologies that allow robotic systems and uncrewed vehicles to perceive their surroundings, communicate, navigate and operate with limited human intervention. Students develop an understanding of the embedded electronics, signal-processing methods and wireless technologies that support autonomous robotic platforms.

Students begin by studying microcontroller applications, including embedded hardware and software, input and output operations, peripheral devices, structured programming, troubleshooting and debugging. They then choose a pathway in either signal processing or wireless communication.

The signal-processing option explores embedded system architecture, analog interfaces, timers, serial communication, interrupts and real-time digital signal processing. Students learn to analyze sampled signals, evaluate system behavior, design digital filters and implement signal-processing applications using specialized hardware and software.

The wireless communication option examines modulation, transmitters, receivers, signal analysis, radio-frequency components, electromagnetic wave propagation, transmission lines, impedance matching and antenna design. Graduates are prepared to support the development of autonomous ground, aerial, marine and other robotic systems that depend on reliable sensing, embedded control and wireless communication.

The Mechanisms and Controls concentration connects mechanical design with the electrical and computational systems used to produce and regulate motion. Students develop a deeper understanding of how robotic mechanisms are designed, how forces affect mechanical components, and how control systems direct machinery to achieve precise and repeatable movement.

Students choose between pathways in mechanism design or mechanism control. The mechanism design option focuses on engineering specifications, computer-aided design, mechanical fits, strength of materials, component selection and failure prevention. Students may study gears, shafts, bearings, belt and chain drives, motors, fatigue, buckling and structural joints as they learn to design durable mechanical assemblies.

The mechanism control option emphasizes fluid power, industrial instrumentation, process control, feedback systems and hydraulic or pneumatic motion control. Students learn to select sensors and actuators, design control circuits, tune controllers, simulate dynamic system behavior, and integrate mechanical components with electronic inputs and outputs.

Graduates of this concentration are prepared to contribute to robotic mechanism design, motion-control development, automated equipment integration, industrial machinery, testing and systems troubleshooting. They are equipped to connect physical machine design with the control strategies required to make robotic systems move safely and effectively.

The Intelligent Manufacturing concentration provides focused study in the technologies used to design products and transform digital designs into manufactured components. Students explore how robotics, computer-controlled machinery, digital production tools and quality systems work together within modern manufacturing environments.

Students choose between pathways in design for manufacturing or manufacturing processes. Both pathways develop an understanding of manufacturing systems, material-removal processes, measurement, assembly, statistical quality control, process planning, CNC programming and automated production.

The design for manufacturing option emphasizes engineering specifications, parametric computer-aided design, mechanical fits, technical drawings, geometric dimensioning and tolerancing, digital manufacturing and product-quality requirements. The manufacturing process option provides additional hands-on study in CNC programming and machining, computer-aided manufacturing, additive manufacturing, material and process selection, production planning, inspection and quality control.

Graduates of this concentration are prepared to connect robotic automation with product design and manufacturing operations. Their skills can support digital manufacturing, production automation, CNC machining, additive manufacturing, process improvement, product quality and the integration of robotic systems within advanced manufacturing facilities.

The IoT and Systems concentration provides focused study in the connected sensors, devices, machines and computing platforms that allow robotic and industrial systems to collect, exchange and act on data. Students develop expertise in industrial instrumentation, data acquisition, automation controls, networking, cybersecurity and the Industrial Internet of Things (IIoT).

Coursework in controls and instrumentation prepares students to evaluate sensors, select data-acquisition systems, understand closed-loop system behavior and tune proportional-integral-derivative controllers. Students learn how physical measurements and control signals are captured, transmitted and used within automated systems.

Through a two-course sequence in industrial IoT, students examine the convergence of information technology and operational technology in smart manufacturing. Topics include industrial networks, communication protocols, reference architectures, host- and network-level security, safety systems, industry standards, and the integration of new and existing industrial equipment.

Advanced coursework introduces edge, hybrid and cloud computing for real-time and batch processing, along with foundational machine learning applications for automated systems. Graduates are prepared to support connected robotics, industrial networking, smart manufacturing infrastructure, systems integration, automation cybersecurity and data-enabled production environments.

The Smart Manufacturing concentration equips students in Robotics Engineering Technology and Mechatronics Engineering Technology with the technical and analytical skills required to design, integrate and optimize intelligent production systems. The concentration connects robotics and automation with industrial networking, machine learning, data analytics and additive manufacturing technologies.

Through coursework in the Industrial Internet of Things (IIoT), students learn how information technology and operational technology systems work together to support industrial data acquisition and connected automation. Topics include industrial networks, communication protocols, system architectures, cybersecurity controls, industry standards, and the reliable integration of devices and infrastructure within modern manufacturing environments.

Students also explore how artificial intelligence, industrial IoT and large manufacturing datasets can support predictive analytics, process optimization, root-cause analysis and predictive maintenance. Coursework in additive manufacturing develops expertise in process and material selection, design for additive manufacturing, production planning, support structures, inspection and quality control.

Through hands-on laboratories and applied projects, students learn to integrate robotics, automation, secure connectivity, AI-driven analytics and advanced production processes into connected manufacturing systems. Graduates are prepared to support Industry 4.0 initiatives that improve productivity, quality, reliability and system performance across modern manufacturing enterprises.