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 Systems
Compulsory activities for Smart Systems & Data Analytics
2° Year activated in the A.Y. 2023/2024
Modules | Credits | TAF | SSD |
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Compulsory activities for Embedded & Iot Systems
Compulsory activities for Robotics Systems
Compulsory activities for Smart Systems & Data Analytics
Modules | Credits | TAF | SSD |
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Compulsory activities for Embedded & Iot Systems
Compulsory activities for Smart Systems & Data Analytics
Modules | Credits | TAF | SSD |
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Compulsory activities for Embedded & Iot Systems
Compulsory activities for Robotics Systems
Compulsory activities for Smart Systems & Data Analytics
Modules | Credits | TAF | SSD |
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3 modules among the following
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.
Mobile robotics (2022/2023)
Teaching code
4S009023
Credits
6
Language
English
Also offered in courses:
- AI in Robotics of the course Master's degree in Artificial intelligence
- AI in Robotics of the course Master's degree in Artificial intelligence
- AI in Robotics of the course Master's degree in Artificial intelligence
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
The teaching is organized as follows:
Teoria
Laboratorio
Learning objectives
This course presents the main issues related to control and planning 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 deigning 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.
To pass the exam, students must demonstrate:
- to have understood the principles behind programming for mobile robots
- to be able to present arguments on the topics of the course in a precise and organic way without digressions
- to know how to apply the acquired knowledge to solve application problems presented in the form of exercises, questions and projects.
Prerequisites and basic notions
No specific requirements.
Program
– Kinematics and dynamics for mobile robots (e.g., non-holonomic constrain, unicycle-like model).
– Navigation for mobile robots: localization and mapping (e.g., Extended Kalman Filter SLAM), trajectory planning (e.g., navigation functions).
– 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).
Bibliography
Didactic methods
Lectures in classrooms and in lab with mobile robotic platforms. The slides used during the lessons and other material (eg, access to code and mobile robotic platforms) will be provided.
Learning assessment procedures
The exam is composed of an oral test and a project that focuses on mobile robot programming.
Evaluation criteria
To pass the exam, students must demonstrate:
- to have understood the principles behind programming for mobile robots
- to be able to present arguments on the topics of the course in a precise and organic way without digressions
- to know how to apply the acquired knowledge to solve application problems presented in the form of exercises, questions and projects.
Criteria for the composition of the final grade
The final mark will be the average of the marks obtained in the oral test and in the project.
Exam language
English