Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Laurea magistrale in Computer Engineering for intelligent Systems - Enrollment from 2025/2026The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
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2° Year activated in the A.Y. 2025/2026
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3 modules among the following| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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3 modules among the following| Modules | Credits | TAF | SSD |
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4 modules among the following:
- 1st year: Advanced visual computing and 3d modeling, Computer vision, Embedded & IoT systems design, Embedded operating systems, Robotics
- 2nd year: Advanced control systemsLegend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Robotics, vision and AI (2024/2025)
Teaching code
4S012361
Credits
6
Language
English
Also offered in courses:
- Robotics, Vision and Control - ROBOTICS of the course Master's degree in Computer Engineering for Robotics and Smart Industry
- Robotics, Vision and Control - VISION AND CONTROL of the course Master's degree in Computer Engineering for Robotics and Smart Industry
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Courses Single
Authorized
The teaching is organized as follows:
Vision and AI
Robotics
Learning objectives
The course aims to provide the following knowledge: theoretical and applicative aspects of control algorithms for vision-based robots, with particular reference to issues of camera-robot calibration, reconstruction, planning and motion control. At the end of the course the student will have to demonstrate the following abilities to apply the knowledge acquired: ability to choose, integrate and implement calibration, 3D reconstruction, planning and control algorithms for vision-guided robotic systems; demonstrate knowledge of the main (a) camera-robot calibration tools; (b) use of range sensors; (c) reconstruction of scenes from rooms; (d) vision-based planning and control. He/she must also have the ability to define the technical specifications to select, integrate and design software modules for vision-based robotic systems and be able to collaborate with professional figures to design vision-based control architectures for complex robotic systems. Finally, they must have the ability to continue their studies independently to follow the technical evolution in the field of robot control based on vision systems.
Prerequisites and basic notions
Dynamic systems, Robotics, Computer Vision
Program
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UL: Robotics
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Topics that will be addressed during the course:
- motion control of a manipulator
- trajectory planning
- robotic vision-based control
During the lab activity, students will implement the algorithms in ROS/Matlab-Simulink and on real robotic manipulators.
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UL: Vision and AI
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3D acquisition systems, registration, meshing,
Image processing, morphological operators, shape properties,
3D analysis, range image acquisition and processing, model fitting,
Hand-eye calibration, rotation, general method and Tsai’s method.
Camera pose estimation, posit method.
Didactic methods
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UL: Robotics
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Frontal lessons for the theoretical part; Lectures with the active involvement of students for the laboratory part.
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UL: Vision and AI
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Lectures, blackboard exercises, laboratory exercises. Talks by professionals from the industrial sector.
Learning assessment procedures
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UL: Robotics
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The exam will consist in the discussion of the homework (HWs) assigned during the semester on the topics developed during the course.
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UL: Vision and AI
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Evaluation and discussion of periodic homework
Evaluation criteria
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UL: Robotics
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To pass the exam, the student must demonstrate:
- to have understood the algorithms for path planning,
- to be able to apply the knowledge acquired during the course to solve the assigned problems,
- be able to present their work and to argue the design choices.
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UL: Vision and AI
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Accuracy of results, awereness of involved topics, clarity and readability of code.
Criteria for the composition of the final grade
Average of the marks in UL: Robotics and in UL: Vision and AI
Exam language
------------------------ UL: Robotics ------------------------ Inglese / English ------------------------ UL: Vision and AI ------------------------ Inglese / English
Sustainable Development Goals - SDGs
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