Instructor: Prof. Mario Selvaggio
Assistant: Ing. Paolo Maisto

• Monday, 4:30-6:30 P.M., Room: SG_A1_P3_A2
• Wednesday, 8:30-10:30 A.M., Room: SG_A1_P3_A2

Lectures are taught in person. Microsoft Teams (MT) platform will be used in case of attendance issues, to share class material, and general course's organization. Students are kindly invited to join the MT class with code ge9pz6t.

Send an e-mail (using the tag [MR2025] in subject) to the instructor or to the assistant(s) to communicate with us. You can book a 1-hour meeting via the appointment scheduling feature provided by google calendar. The assistantship will be provided in person and/or via the MT platform.

The module aims to introduce students to the main methodologies used to enhance the level of autonomy in mobile robotic systems. The core topics covered in the course include: fundamentals of robotics; modeling and control of wheeled mobile robots; perception for mobile robotics; motion planning; autonomous navigation techniques. These topics will first be addressed from their theoretical foundations and subsequently explored through advanced simulated case studies implemented using MATLAB. By the end of the module, students will have developed an integrated understanding of the key components contributing to mobile robot autonomy and will be able to apply these methodologies in complex simulated scenarios.

The course provides students with the theoretical and methodological foundations for the design and development of planning and control algorithms for autonomous wheeled mobile robots. In particular, students will acquire knowledge of: kinematic and dynamic modeling of wheeled mobile robots; pose estimation techniques based on wheel encoders and proprioceptive sensors; sensor integration methods for state reconstruction; motion planning algorithms in known and partially known environments; control input generation for autonomous navigation. Students will be able to formally describe the mathematical models and algorithms underlying autonomous navigation and discuss their practical implementation in a simulated environment, critically analyzing the adopted design choices.

Students will be required to demonstrate the ability to critically analyze the architecture and performance of navigation and localization systems for wheeled mobile robots, evaluating modeling assumptions, state estimation strategies, and sensor integration approaches. In particular, students will be expected to: interpret and discuss design choices related to kinematic modeling, state estimation, and motion planning; assess system limitations and sources of uncertainty; propose technically justified improvements or alternative solutions; generalize and transfer the acquired knowledge to different robotic platforms characterized by diverse kinematic structures and sensing configurations. Students must also demonstrate independent judgment and the ability to apply theoretical principles to novel or previously unseen robotic scenarios.

• Introduction and overview
• Robotics foundations
• Wheeled robots
• Perception
• Motion planning
• Autonomous navigation

• B. Siciliano, L. Sciavicco, L. Villani, G. Oriolo “Robotics – Modelling, Planning, and Control,” Springer, London, 2009, ISBN: 978-1-84628-641-4
• R. Siegwart, I. R. Nourbakhsh, D. Scaramuzza, “Introduction to Autonomous Mobile Robots,” 2nd edition, MIT Press, 2011, ISBN: 9780262015356
• K. M. Lynch, F. C. Park “Modern Robotics: Mechanics, Planning, and Control,” Cambridge University Press, 2017, ISBN: 9781107156302
• H. Choset, K. Lynch, S. Hutchinson, G. Kantor, W. Burgard, L. Kavraki, S. Thrun, “Principles of Robot Motion: Theory, Algorithms and Implementations,” MIT Press, 2005

The exam consists of a midterm and final written part plus an oral discussion.

Here is a tentative schedule of lectures. Readings and assignments will be added as they become available.