Instructor: Prof. Mario Selvaggio

• TBD
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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 TBD.

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.

This module aims to introduce students to the primary methodologies used to improve the autonomy level of mobile robots. Advanced theory about modelling and control of mobile robots, planning obstacle-free trajectories in unknown environments, generating obstacles grid maps and performing simultaneous localization and mapping will be the main concepts considered in this module. Information on different kinds of sensors used to perform autonomous navigation of mobile robots is provided. These topics will be studied and analyzed starting from their basic theory and later applied in advanced simulated case studies using MATLAB software.

The course provides students with the methodology and theory for designing and developing autonomous wheeled mobile robot planners and controllers. The student will learn how to implement autonomous navigation mathematically and practically for mobile robots starting from the reconstruction of its pose with wheel encoders, the generation of control inputs, and the knowledge of the environment. The students will demonstrate their knowledge by discussing a practical application developed in a simulated environment.

The student must show his/her ability to analyze a navigation and localization system implemented for wheeled mobile robots. In this context, the student will demonstrate his/her critical thinking skills. Finally, the students must demonstrate how to apply the acquired knowledge to different mechanical systems endowed with different sensors.

• Modelling and control of mobile robots under different kinds of locomotion.
• Generation of obstacle free path for mobile robots with and without motion constraints. Solution complete path planners (A*, D*) and sample-based path planners (PRM and RRT). Environment representation.
• Mobile robot sensors.
  • Characteristic of different sensors used in mobile robot navigation: sonar, LIDARs, GPS.
  • Sensors to calculate the orientation of the robot: IMU and sensor fusion.
  • Computer vision.
• Localization, and simultaneous localization and mapping methods for mobile robots navigating with LIDAR sensors.

• Roland Siegwart, Illah R. Nourbakhsh, and Davide Scaramuzza, Introduction to Autonomous Mobile Robots, second edition, MIT Press.
• Sebastian Thrun, Wolfram Burgard and Dieter Fox, Probabilistic Robotics, MIT Press.
• B. Siciliano, L. Sciavicco, L. Villani, G. Oriolo, Robotics - Modelling, Planning and Control, Springer, London, 2009. No textbook recommended. Links to useful online resources will be shared along the course.

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