The course aims at providing necessary skills to conceive, develop and complete the software design of robots and autonomous systems. Teaching is characterized by a highly experimental approach by resorting to the ROS framework.
Curriculum
teacher profile teaching materials
2. Introduction to the Robot Operating System (ROS) 2 middleware: nodes, topics, services, actions and parameters.
3. Coordinate frames and transformations: representations of rotations and transformations, and how ROS 2 handles them.
4. Robot models and kinematics: kinematics of differential-drive robots and other mobile platforms, robot description and simulation.
5. Actuators and sensors: how motors and sensors work (encoders, IMUs, LiDARs, cameras) and the sources of measurement error.
6. Feedback control: implementation and tuning of discrete controllers for mobile-robot motion.
7. Perception algorithms: classes of perception algorithms for robotics, image (2D) and point-cloud (3D) processing.
8. State estimation and localization: filters for sensor fusion and localization.
9. Mapping: environment representation, map building, and algorithms for simultaneous localization and mapping.
10. Motion and mission planning: path-planning algorithms, navigation with Nav2, and mission management with finite-state machines and behaviour trees.
- Nikolaus Correll, Bradley Hayes, Christoffer Heckman, and Alessandro Roncone, “Introduction to Autonomous Robots: Mechanisms, Sensors, Actuators, and Algorithms”, MIT Press, 2022, https://github.com/Introduction-to-Autonomous-Robots/Introduction-to-Autonomous-Robots/releases
- Mordechai Ben-Ari, Francesco Mondada, “Elements of Robotics”, Springer Cham, 2017, https://link.springer.com/book/10.1007/978-3-319-62533-1
- Notes of the teacher
Programme
1. Introduction to robotics: robot components, the Sense-Think-Act model, mobile robot classes, examples of robots.2. Introduction to the Robot Operating System (ROS) 2 middleware: nodes, topics, services, actions and parameters.
3. Coordinate frames and transformations: representations of rotations and transformations, and how ROS 2 handles them.
4. Robot models and kinematics: kinematics of differential-drive robots and other mobile platforms, robot description and simulation.
5. Actuators and sensors: how motors and sensors work (encoders, IMUs, LiDARs, cameras) and the sources of measurement error.
6. Feedback control: implementation and tuning of discrete controllers for mobile-robot motion.
7. Perception algorithms: classes of perception algorithms for robotics, image (2D) and point-cloud (3D) processing.
8. State estimation and localization: filters for sensor fusion and localization.
9. Mapping: environment representation, map building, and algorithms for simultaneous localization and mapping.
10. Motion and mission planning: path-planning algorithms, navigation with Nav2, and mission management with finite-state machines and behaviour trees.
Prerequisiti
There are no mandatory prerequisite courses. Students are nevertheless advised to have taken Fundamentals of Automatic Control and Fundamentals of Artificial Intelligence. The linear algebra, probability and programming knowledge acquired in the first years of the degree is also useful.Core Documentation
- Stefano Carpin, “Mobile Robotics: Theory and Practice”, 2025, https://robotics.ucmerced.edu/mobile-robotics-textbook- Nikolaus Correll, Bradley Hayes, Christoffer Heckman, and Alessandro Roncone, “Introduction to Autonomous Robots: Mechanisms, Sensors, Actuators, and Algorithms”, MIT Press, 2022, https://github.com/Introduction-to-Autonomous-Robots/Introduction-to-Autonomous-Robots/releases
- Mordechai Ben-Ari, Francesco Mondada, “Elements of Robotics”, Springer Cham, 2017, https://link.springer.com/book/10.1007/978-3-319-62533-1
- Notes of the teacher
Attendance
Optional attendanceType of evaluation
Student assessment is based on the Dublin Descriptors and takes into account the level of knowledge, understanding, and critical processing of the topics covered in the course. Students who demonstrate that they have acquired the fundamental concepts and are able to apply them in simple contexts will receive a passing grade. Average grades will be awarded to those who, in addition to mastering the basics, show the ability to analyze and independently elaborate on more complex concepts. The highest grades are reserved for students who demonstrate in-depth understanding, synthesis skills, critical thinking, and autonomy in solving problems, including new ones or those not explicitly addressed during the course. teacher profile teaching materials
2. Introduction to the Robot Operating System (ROS) 2 middleware: nodes, topics, services, actions and parameters.
3. Coordinate frames and transformations: representations of rotations and transformations, and how ROS 2 handles them.
4. Robot models and kinematics: kinematics of differential-drive robots and other mobile platforms, robot description and simulation.
5. Actuators and sensors: how motors and sensors work (encoders, IMUs, LiDARs, cameras) and the sources of measurement error.
6. Feedback control: implementation and tuning of discrete controllers for mobile-robot motion.
7. Perception algorithms: classes of perception algorithms for robotics, image (2D) and point-cloud (3D) processing.
8. State estimation and localization: filters for sensor fusion and localization.
9. Mapping: environment representation, map building, and algorithms for simultaneous localization and mapping.
10. Motion and mission planning: path-planning algorithms, navigation with Nav2, and mission management with finite-state machines and behaviour trees.
- Nikolaus Correll, Bradley Hayes, Christoffer Heckman, and Alessandro Roncone, “Introduction to Autonomous Robots: Mechanisms, Sensors, Actuators, and Algorithms”, MIT Press, 2022, https://github.com/Introduction-to-Autonomous-Robots/Introduction-to-Autonomous-Robots/releases
- Mordechai Ben-Ari, Francesco Mondada, “Elements of Robotics”, Springer Cham, 2017, https://link.springer.com/book/10.1007/978-3-319-62533-1
- Notes of the teacher
Mutuazione: 20810516-2 ELEMENTI DI ROBOTICA II MODULO in Ingegneria Informatica e dell'Intelligenza Artificiale L-8 R LIPPI MARTINA
Programme
1. Introduction to robotics: robot components, the Sense-Think-Act model, mobile robot classes, examples of robots.2. Introduction to the Robot Operating System (ROS) 2 middleware: nodes, topics, services, actions and parameters.
3. Coordinate frames and transformations: representations of rotations and transformations, and how ROS 2 handles them.
4. Robot models and kinematics: kinematics of differential-drive robots and other mobile platforms, robot description and simulation.
5. Actuators and sensors: how motors and sensors work (encoders, IMUs, LiDARs, cameras) and the sources of measurement error.
6. Feedback control: implementation and tuning of discrete controllers for mobile-robot motion.
7. Perception algorithms: classes of perception algorithms for robotics, image (2D) and point-cloud (3D) processing.
8. State estimation and localization: filters for sensor fusion and localization.
9. Mapping: environment representation, map building, and algorithms for simultaneous localization and mapping.
10. Motion and mission planning: path-planning algorithms, navigation with Nav2, and mission management with finite-state machines and behaviour trees.
Prerequisiti
There are no mandatory prerequisite courses. Students are nevertheless advised to have taken Fundamentals of Automatic Control and Fundamentals of Artificial Intelligence. The linear algebra, probability and programming knowledge acquired in the first years of the degree is also useful.Core Documentation
- Stefano Carpin, “Mobile Robotics: Theory and Practice”, 2025, https://robotics.ucmerced.edu/mobile-robotics-textbook- Nikolaus Correll, Bradley Hayes, Christoffer Heckman, and Alessandro Roncone, “Introduction to Autonomous Robots: Mechanisms, Sensors, Actuators, and Algorithms”, MIT Press, 2022, https://github.com/Introduction-to-Autonomous-Robots/Introduction-to-Autonomous-Robots/releases
- Mordechai Ben-Ari, Francesco Mondada, “Elements of Robotics”, Springer Cham, 2017, https://link.springer.com/book/10.1007/978-3-319-62533-1
- Notes of the teacher
Attendance
Optional attendanceType of evaluation
Student assessment is based on the Dublin Descriptors and takes into account the level of knowledge, understanding, and critical processing of the topics covered in the course. Students who demonstrate that they have acquired the fundamental concepts and are able to apply them in simple contexts will receive a passing grade. Average grades will be awarded to those who, in addition to mastering the basics, show the ability to analyze and independently elaborate on more complex concepts. The highest grades are reserved for students who demonstrate in-depth understanding, synthesis skills, critical thinking, and autonomy in solving problems, including new ones or those not explicitly addressed during the course.