This advanced engineering master's course provides a comprehensive understanding of the fundamental concepts, methodologies, and technologies associated with sensors, embedded systems, and algorithms for service robotics. The course focuses on the working principles of autonomous navigation for mobile robots, particularly focusing on wheeled platforms. The topics presented explore each of the fundamental modules of an autonomous navigation system: robot localization, mapping, control, and path planning. Probabilistic models of robot’s motion and sensors are presented in a complete theoretical framework to consider the uncertainty of real-world robotics processes. The course aims to equip students with the necessary knowledge and skills to design, develop, and deploy intelligent robotic systems capable of performing a wide range of services in diverse environments. Students will be provided with both theoretical and practical aspects of the topics, favouring a hands-on experience in the laboratory to practice with robotic platforms and sensors.

Throughout the course, students will engage in practical projects and laboratory sessions, working with hardware platforms and simulation tools commonly used in service robotics research and development. They will collaborate in teams to design and implement innovative robotic solutions, leveraging their knowledge of sensors, embedded systems, and algorithms. By the end of the course • Students will have a deep conceptual understanding of autonomous mobile robots’ systems. • They will experiment the integration between sensors, embedded systems, and algorithms in the context of service robotics. • They will be proficient in designing and implementing intelligent robotic systems capable of perceiving their environment, making informed decisions, and executing complex tasks. • They will acquire practical know-how with standard robotics programming languages and frameworks.

Mathematics: linear algebra and matrix analysis, rotations and reference frames, quaternions, probability theory, linearization, and Taylor expansion. Control systems: basic concepts of closed loop control. Software: basic programming skills (Python, object-oriented programming) Electronics: embedded systems, sensors integration

Theory and tutorials [39 h]: - Service robotics introduction [1.5h] Typical missions and requirements - Programming background: introduction to object-oriented programming in Python [3h] NumPy: math operations with multi-dimensional arrays, slicing and indexing Classes and objects - Mobile robots’ architecture (locomotion, power train, battery, BMS, etc): [1.5h] UGV UAV HW/SW architectures (RT low-layer, navigation layer, etc) Wheeled robotic platforms - ROS/ROS 2 [3h] Introduction Nodes, topics and services/actions How to write a ROS Node (tutorial) Advanced tutorials - Simulation setup and tools [1.5h] Gazebo Rviz - Perception & sensors for mobile robots: [1.5h] Proprioperception: Wheel Encoder, IMU Exteroperception: Proximity sensors: LiDAR, Cameras, Ultrasound, Infrared - Probabilistic motion models [3h] Odometry model Velocity based model - Probabilistic sensors models [3h] - Localization: [9h] Bayes filter Kalman Filter, Extended Kalman Filter and Unscented Kalman Filter Particle filter and Monte Carlo Localization - Introduction to NAV 2 navigation stack [1.5h] - Mapping: [3h] Grid maps and Mapping with known poses Introduction to SLAM - Path and trajectory planning: [3h] Search-based Global Planner: A*, Dijkstra - Motion control, Obstacle avoidance and Trajectory tracking: [3 h] Local planner/controller: DWA, Pure pursuit Laboratory [21h]: - ROS/ROS 2, Gazebo/Rviz [6h] Nodes, topics Services/actions Simulation in Gazebo (sensors, platform, worlds) - Hands-on first practice with a real robot [3h] Bring-up Navigation test - Robot Localization [6h] EKF position tracking Particle Filter Localization - NAV 2 / SLAM Toolbox [6h] Localization & mapping Planning & control

This is the 6CFU version (for free credits) of the 01HFWQW 8CFU course

The course is composed by a theory part typically done in classrooms and some technical tutorials about HW/SW tools that will be used during the course itself (4 CFU). The course also includes 7 experimental laboratory exercises (2 CFU) to be performed at the LED laboratories. The labs are organized in groups of 3/4 students. Groups must prepare a technical report for the three main lab activities, that will be evaluated and will be part of the final exam.

Suggested textbooks: - Probabilistic Robotics: Wolfram Burgard, Dieter Fox, Sebastian Thrun (2005) http://www.probabilistic-robotics.org/ - Introduction to Autonomous Mobile Robots: (2nd Edition) Roland Siegwart, Illah Reza Nourbakhsh, Davide Scaramuzza (2011) https://mitpress.mit.edu/9780262015356/introduction-to-autonomous-mobile-robots/ - A Concise Introduction to Robot Programming with ROS2, Rico, Taylor & Francis Ltd, 2022

Slides; Dispense; Esercitazioni di laboratorio; Video lezioni dell’anno corrente; Video lezioni tratte da anni precedenti; Strumenti di simulazione;

Modalità di esame: Prova scritta (in aula); Prova orale obbligatoria; Prova pratica di laboratorio; Elaborato progettuale in gruppo;

Exam: Written test; Compulsory oral exam; Practical lab skills test; Group project;

... Written test (in presence); Practical lab reports (to be delivered according to the deadlines defined with the annual calendar). The final exam consists of two distinct parts: - evaluation of the experimental activities carried out in the laboratory (offline evaluation of each technical report) - MAX 17 points – (MIN 10 points to pass the exam) - written exam focused ONLY on the theoretical aspects of the course (90 minutes, 4/5 questions, no material admitted) - MAX 16 points – (MIN 8 points to pass the exam)

Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.