Mobile robotics (2020/2021)

Course code
Alessandro Farinelli
Academic sector
Language of instruction
Teaching is organised as follows:
Activity Credits Period Academic staff Timetable
Teoria 5 II semestre Alessandro Farinelli

Go to lesson schedule

Laboratorio 1 II semestre Alessandro Farinelli

Go to lesson schedule

Learning outcomes

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.


– 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., ROS), simulation environments for empirical evaluation (e.g., Gazebo/Stageros/Vrep), validation on simple mobile platforms (e.g., turtlebot3).

Assessment methods and criteria

The exam is composed of a written test and a project that focuses on mobile robot programming.