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

ModulesCreditsTAFSSD
9
B
ING-INF/04
Compulsory courses for Embedded & IoT Systems
Compulsory courses for Robotics systems
6
B/C
INF/01
6
B/C
ING-INF/05
Compulsory courses for Smart systems &data analytics
6
B/C
INF/01 ,ING-INF/06
6
B/C
ING-INF/05

2° Year  activated in the A.Y. 2021/2022

ModulesCreditsTAFSSD
Compulsory courses for Embedded & IoT Systems
Compulsory courses for Robotics systems
Compulsory courses for Smart systems &data analytics
6
B/C
ING-INF/05
Final exam
24
E
-
ModulesCreditsTAFSSD
9
B
ING-INF/04
Compulsory courses for Embedded & IoT Systems
Compulsory courses for Robotics systems
6
B/C
INF/01
6
B/C
ING-INF/05
Compulsory courses for Smart systems &data analytics
6
B/C
INF/01 ,ING-INF/06
6
B/C
ING-INF/05
activated in the A.Y. 2021/2022
ModulesCreditsTAFSSD
Compulsory courses for Embedded & IoT Systems
Compulsory courses for Robotics systems
Compulsory courses for Smart systems &data analytics
6
B/C
ING-INF/05
Final exam
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
Between the years: 1°- 2°
Other activities
3
F
-
Between the years: 1°- 2°
Training
3
F
-

Legend | 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.




S Placements in companies, public or private institutions and professional associations

Teaching code

4S009019

Credits

6

Coordinator

Riccardo Muradore

Language

English en

The teaching is organized as follows:

ROBOTICS en

Credits

3

Period

See the unit page

Academic staff

See the unit page

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
- 3D reconstruction of the working area
- camera-robot calibration
- robotic vision-based control
During the lab activity, students will implement the algorithms in ROS/Matlab-Simulink and on real robotic manipulators.

Bibliography

Reference texts
Author Title Publishing house Year ISBN Notes
Peter Corke Robotics, Vision and Control Springer Nature 2017 978-3-319-54412-0

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.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE