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 understanding of 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.

Physics: power and energy, basic electromagnetics. Mathematics: algebra of complex numbers, linear algebra and matrix analysis, algebraic linear systems, first-order linear differential equations, basis of Laplace transform. Control systems: basic elements. Software: embedded software, basic programming skills in C++, Python Electronics: embedded systems, sensors integration

Theory and tutorials [50 h]: - Service robotics introduction [1.5h] Typical missions and requirements - Mobile robots architecture (locomotion, power train, battery, BMS, etc): [3h] UGV UAV HW/SW architectures (RT low-layer, navigation layer, etc) - Technical background: [1.5h] Linear algebra Robot control paradigms (general overview of the pipeline) - Wheeled Mobile Robots: [3h] Locomotion models of mobile robots Differential drive, Ackermann drive Synchronous drive, XR4000 Drive Mechanum Wheels Tracked Vehicles - ROS/ROS 2 [3h] Introduction Nodes, topics and services/actions How to write a Node - 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 - Introduction to probability [3h] - Probabilistic motion models [3h] Odometry model Velocity based model - Probabilistic sensors models [1.5h] - Introduction to NAV2 navigation stack [3h] - Localization: [6h] Odometric localization Bayes filter Kalman Filter and Extended Kalman Filter Particle filter and Monte Carlo Localization - Final project: rules and proposals presentation [1.5] - Mapping: [3h] Grid maps and Mapping with known poses Introduction to SLAM Landmark-based localization and SLAM (optional) - Path and trajectory planning: [3h] Global planner: A*, Dijkstra Probabilistic approaches: Probabilistic Roadmap, RRT* - Motion control - Introduction and Trajectory tracking: [3h] Local planner/controller: DWA, TEB Laboratory [30h]: - ROS/ROS 2, Gazebo/Rviz [9h] Nodes, topics Services/actions Custom Gazebo settings (sensors, platform, worlds) - NAV2 [9h] Localization & mapping Planning & control - Hands-on a real robot [3h] Bringup Navigation test - Project with real robot [9h]

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 (5 CFU). The course includes also 4 experimental laboratory exercises (3 CFU) to be performed at the LED laboratories. The labs are organized in groups of 3/4 students. For each lab experiment, groups must prepare a technical report that will be evaluated and will be part of the final exam together with a comprehensive final experiment.

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

Lecture slides; Lecture notes; Lab exercises; Video lectures (current year); Simulation tools;

Exam: Written test; Optional oral exam; Practical lab skills test;

The final exam consists of two distinct parts: - evaluation of the experimental final project carried out in the laboratory (offline evaluation of each technical report) - MAX 20 points - written exam focused ONLY on the theoretical aspects of the course (90 minutes, 4/5 questions, no material admitted) - MAX 13 points The resulting score can be registered or further integrated (-3 to +3) with a optional oral session.

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.