Autonomous Robots
Academic Year 2026/2027 - Teacher: GIOVANNI MUSCATOExpected Learning Outcomes
Knowledge and Understanding
The course aims to provide students with knowledge of the operation of autonomous robotic systems.
Particular attention is devoted to robotic manipulators and mobile robots for industrial and service applications.
Kinematics and programming of industrial robots. Kinematics, trajectory planning, localization, and navigation of mobile robots.
Applying Knowledge and Understanding
By the end of the course, students will be able to analyze a robotic system and design its autonomous navigation system.
Making Judgements
Students will acquire the skills required to analyze a robotic system and its components, and will be able to propose solutions to problems requiring the use of robotic systems.
Communication Skills
Students will acquire the language skills and terminology specific to robotic systems and will be able to communicate their characteristics, performance, and operating principles to both experts in the field and non-specialist audiences.
Learning Skills
The knowledge and skills acquired during the course will enable students to further develop their studies towards the analysis and design of more complex robotic systems in a self-directed and autonomous manner.
Course Structure
The course is delivered through lectures supported by slides, which are available on the Studium platform.
The course also includes a series of computer-based exercises for robot simulation, as well as laboratory activities aimed at developing practical experience in the control and programming of robotic systems.
Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.
Required Prerequisites
Basic knowledge on Automatic Control, Electronics, Programming
Attendance of Lessons
Attendance of lecture is not compulsory, but strongly suggested to take the exam.
Attendance is compulsory as regard laboratory exercises.
Detailed Course Content
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Introduction to robotics: industrial, service, and field robotics. Fundamentals of robotic manipulators. Direct and inverse kinematics of manipulators. |
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Introduction to mobile robots: definition and features of a mobile robot, types of mobile robots and their applications. |
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Mobile robot kinematics: types of drive systems, non-holonomy, differential drive model |
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Mobile robot navigation and motion control methods: types of navigation methods and problems, move to a point, follow a line, follow a path, move to a pose. |
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Sensors and perception in mobile robotics: encoders, Inertial Measurement Units (IMUs), Global Navigation Satellite Systems (GNSS), Laser Rangefinders, Vision systems |
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Mobile robots localization: odometry, map-based localization, map and belief representations, types of localization problems |
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Probabilistic localization: Markov localization (Monte Carlo and Grid-based localization), Kalman filter localization |
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Path planning algorithms: grid-based (Dijkstra, A*, D*), Artificial Potential Fields, sample-based (PRM, RRT). |
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Introduction to Robot Operating System (ROS): overview of the framework, basic concepts (nodes, topics, services, actions), introduction to workspaces and software organization |
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Lab exercises on selected mobile robot navigation algorithms and modules implementation in ROS. |
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Lab projects on robots with ROS. |
Textbook Information
[1] B. Siciliano, L. Sciavicco, L. Villani, G. Oriolo,“Robotica”, Mc Graw-Hill Italia
[2] B. Siciliano, L. Sciavicco, L. Villani, G. Oriolo,“Robotics”, Springer
[3] R. Siegwart, I. Nourbakhsh, “Introduction to Autonomous Mobile Robots”, MIT Press
[4] Thrun, Burgard, Fox, "Probabilistic robotics", MIT Press
[5] Dispense del corso su Studium
Course Planning
| Subjects | Text References | |
|---|---|---|
| 1 | Introduction. Applications of robots. (2 h) | [2] |
| 2 | Direct kinematics (4 h) | [2] |
| 3 | Inverse kinematics (3 h) | [2] |
| 4 | Introduction to mobile robots (4 h) | [3] |
| 5 | Mobile robots localization (4 h) | [3],[4] |
| 6 | Mobile robots mapping (2 h) | [3],[4] |
| 7 | Markov localization Kalman filter localization (4 h) | [4] |
| 8 | Mobile robots Control (3 h) | [3] |
| 9 | KUKA and AUBO manipulator programming (2 h) | [5] |
| 10 | Mobile robots laboratory exercise. Examples of robots (15 h) | [5] |
| 11 | Robotic sensors actuators overview and exercise (3 h) | [5] |
| 12 | ROS programming (4 h) | [5] |
Learning Assessment
Learning Assessment Procedures
The exam consists in the presentation of the laboratory experiments performed, in a report and in an oral dissertation.
Learning assessment may also be carried out on line, should the conditions require it.
Examples of frequently asked questions and / or exercises
Direct and inverse kinematics. Kalman filter. Markov localization. Path planning.