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.
Type D and Type F activities
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 2° | Introduction to quantum mechanics for quantum computing | D |
Claudia Daffara
(Coordinator)
|
| 2° | Introduction to smart contract programming for ethereum | D |
Sara Migliorini
(Coordinator)
|
| 2° | BEYOND ARDUINO: FROM PROTOTYPE TO PRODUCT WITH STM MICROCONTROLLER | D |
Franco Fummi
(Coordinator)
|
| 2° | APP REACT PLANNING | D |
Graziano Pravadelli
(Coordinator)
|
| 2° | HW components design on FPGA | D |
Franco Fummi
(Coordinator)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 2° | LaTeX Language | D |
Enrico Gregorio
(Coordinator)
|
| 2° | Rapid prototyping on Arduino | D |
Franco Fummi
(Coordinator)
|
| 2° | Programming Challanges | D |
Romeo Rizzi
(Coordinator)
|
| 2° | Protection of intangible assets (SW and invention)between industrial law and copyright | D |
Mila Dalla Preda
(Coordinator)
|
Mobile robotics (2024/2025)
Teaching code
4S009023
Credits
6
Coordinator
Not yet assigned
Language
English
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Courses Single
Authorized
The teaching is organized as follows:
Teoria
Credits
5
Period
Semester 2
Academic staff
Alessandro Farinelli
Laboratorio
Credits
1
Period
Semester 2
Academic staff
Daniele Meli
Learning objectives
This course presents the main issues related to Artificial Intelligence techniques for mobile robotic platforms. The objective is to provide the students with the ability to design, apply and evaluate algorithms that allow mobile robotic platforms to interact with the surrounding environment by performing complex tasks with a high level of autonomy. At the end of the course the students must demonstrate to understand the fundamental concepts related to localization, trajectory planning, task planning, decision-making under uncertainty and machine learning in the context of mobile robotic platforms. Moreover, the students must demonstrate to be able to work with the main development tools for mobile robotic applications and to be able to define technical specifications for designing and integrating software modules for mobile robotic platforms. The students must also be able to deal with professional figures to design solutions for the high level control of mobile robotic platforms and to continue the studies independently following the technical evolution in the field of mobile robotics and developing innovative approaches to improve the state of the art.
Program
– Localization and mapping (e.g., recursive state estimation)
– Motion planning for mobile robots (e.g., path planning, obstacle avoidance);
– Decision-making under uncertainty (e.g., Markov Decision Process) .
– Reinforcement learning for mobile robotic platforms (e.g., model-based and model free approaches, Deep RL).
– Lab: implementation of autonomous behaviors for mobile robotic platforms using state of the art development toolkits (e.g., ROS2), simulation environments for empirical evaluation (e.g., Unity), validation on simple mobile platforms (e.g., turtlebot3).
Learning assessment procedures
The exam consists of an oral test focused on the laboratory activities and a second test that can be chosen between two options: i) a project focused on the implementation of some of the techniques studied during the course; ii) an oral exam focused on the topics studied during the course.
