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
The 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
| Modules | Credits | TAF | SSD |
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Compulsory activities for Embedded & Iot SystemsCompulsory activities for Smart Systems & Data Analytics2° Year activated in the A.Y. 2022/2023
| Modules | Credits | TAF | SSD |
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Compulsory activities for Embedded & Iot SystemsCompulsory activities for Robotics SystemsCompulsory activities for Smart Systems & Data Analytics| Modules | Credits | TAF | SSD |
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Compulsory activities for Embedded & Iot SystemsCompulsory activities for Smart Systems & Data Analytics| Modules | Credits | TAF | SSD |
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Compulsory activities for Embedded & Iot SystemsCompulsory activities for Robotics SystemsCompulsory activities for Smart Systems & Data Analytics| Modules | Credits | TAF | SSD |
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3 modules among the followingLegend | 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 control (2021/2022)
The teaching is organized as follows:
Learning outcomes
The course aims to provide the following knowledge: theoretical and application aspects of control algorithms for vision-based robots, with particular focus on camera-robot calibration, reconstruction, planning and movement control. At the end of the course the student will have to demonstrate that s/he has the following skills to apply the acquired knowledge: ability to choose, integrate and implement calibration algorithms, 3D reconstruction, planning and control for vision-guided robotic systems; demonstrate knowledge of the main tools of (a) camera-robot calibration; (b) use of range sensors; (c) reconstruction of scenes from rooms; (d) vision-based planning and control. Student 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 deal with professional figures to design vision-based control architectures for complex robotic systems. Finally, student must have the ability to continue his studies independently to follow the technical evolution in the field of robot control based on vision systems.
Program
Topics that will be addressed during the course:
- motion control of a manipulator
- trajectory planning
- robotic vision-based control
- 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.
During the lab activity, students will implement the algorithms in ROS/Matlab-Simulink and on real robotic manipulators.
Bibliography
Examination Methods
The exam will consist of a project addressing some topics discussed during the course. The student should have to implement in ROS (and/or in Matlab/Simulink) an algorithm, test it, and prepare a brief technical document explaining his/her work.
To pass the exam, the student should:
- have understood the relationship among robotics, vision and control,
- be able to use the knowledge acquired during the course to solve the assigned problem,
- be able to describe their work by explaining and motivating the design choices.
The final grade will be the average of the grade (homework and exposure during the oral exam) of the part of 'Robotics' and that of 'Vision and Control'.
