Connect intelligence with perception and the physical world.
Robotics Fundamentals
Prerequisite:
Basic programming knowledge recommended
Python or C/C++ familiarity is helpful but not required
Suitable For:
- Students entering robotics for the first time
- Software developers moving into robotics
- AI engineers who want to understand physical systems
- Engineers interested in automation and intelligent machines
- Learners preparing for ROS, robot perception, or advanced robotics courses
Description:
Build a practical foundation in robotics by understanding how robots sense, move, make decisions, and interact with the physical world. This course introduces the core concepts of robot hardware, control, sensing, kinematics, and software architecture through practical examples and small projects.
Students Will Learn:
- Understand the main components of a robotic system
- Work with actuators, motors, and common robotic sensors
- Understand coordinate systems and robot motion
- Learn basic forward and inverse kinematics
- Understand feedback and closed-loop control
- Read and process basic sensor data
- Understand mobile robots and robotic manipulators
- Connect software with robotic hardware
- Understand the role of perception and AI in robotics
- Build small robotics projects and simulations
Syllabus:
- Introduction to robotics
- Types of robots and real-world applications
- Robot system architecture
- Sensors, actuators, and controllers
- Motors and servo systems
- Coordinate frames and transformations
- Degrees of freedom
- Position, orientation, and pose
- Forward kinematics
- Introduction to inverse kinematics
- Mobile robot fundamentals
- Robotic manipulators
- Grippers and end effectors
- Basic motion planning concepts
- Feedback and closed-loop control
- PID control fundamentals
- Reading sensor data
- Distance, proximity, and range sensors
- Cameras in robotics
- Introduction to robot perception
- Hardware-software interfaces
- Introduction to ROS and ROS2 concepts
- Simulation fundamentals
- Safety considerations in robotics
- Practical robotics exercises
- Final robotics project
Machine Vision for Industrial Applications
Prerequisite:
Python Programming – Fundamentals
Suitable For:
- Engineers working in manufacturing and automation
- Computer vision developers
- Quality inspection and industrial automation professionals
- AI engineers moving into industrial vision
- Students interested in real-world computer vision applications
Description:
Learn how machine vision systems are designed and deployed for industrial applications such as inspection, measurement, defect detection, localization, and automation. The course combines image processing, cameras, optics, OpenCV, and modern AI-based vision techniques with a strong focus on practical industrial use cases.
Students Will Learn:
- Understand the architecture of an industrial machine vision system
- Select appropriate cameras, lenses, and lighting
- Acquire and process image data
- Use NumPy and OpenCV for practical image processing
- Apply filtering, thresholding, and edge detection
- Detect, segment, and measure objects
- Understand semantic and instance segmentation
- Perform defect detection and visual inspection
- Work with OCR and industrial text recognition
- Understand classical and deep learning-based vision approaches
- Design reliable machine vision pipelines
- Evaluate system accuracy and robustness
- Build practical industrial inspection projects
Syllabus:
- Introduction to machine vision
- Industrial machine vision applications
- Machine vision system architecture
- Digital image fundamentals
- Image representation and color spaces
- Industrial cameras
- Camera interfaces and acquisition
- Resolution, frame rate, and exposure
- Lens selection and field of view
- Lighting techniques for machine vision
- Image acquisition workflow
- NumPy for image processing
- OpenCV fundamentals
- Image resizing and geometric transformations
- Filtering and noise reduction
- Thresholding and binarization
- Edge detection
- Morphological operations
- Contours and connected components
- Feature extraction
- Object detection fundamentals
- Classical object detection methods
- Introduction to deep learning-based detection
- Semantic segmentation
- Instance segmentation
- Measurement and geometric analysis
- OCR and text recognition
- Defect detection
- Surface and component inspection
- Quality-control workflows
- Dataset collection and annotation
- Model evaluation and failure analysis
- Accuracy, latency, and robustness
- Industrial deployment considerations
- Integration with automation systems
- Practical machine vision projects
- Final industrial inspection project
Course content can be tailored to the goals and needs of individuals, teams, and organizations, from focused short courses covering selected topics to comprehensive programs that combine multiple subjects into a customized learning path.