Academic Year 2022/2023

  • Docente: Gianluca Palli
  • Credits: 6
  • SSD: ING-INF/04
  • Language: English
  • Teaching Mode: Traditional lectures
  • Campus: Bologna
  • Corso: Second cycle degree programme (LM) in Automation Engineering (cod. 8891)

Learning outcomes

The course introduces to the field of mobile robots and mobile manipulators, focusing mainly on terrestrial robots but with mention to aerial and underwater robots. The course will focus on mobile robots modelling and control aspects, as well as the task and trajectory planning for these robots. The problem of perception and sensing of unknown environments will be addressed from both the technological and methodological point of view, and the main algorithms for the solution of the navigation and localization problems will be introduced. The control aspects for redundant robot will be considered, with focus on multiplicity of tasks with different priority and the related control strategies, such as hierarchical control. The aspects related to workspace sharing with other robots and humans will be investigated by introducing basic concepts of collaborative robotics and safety. The implementation in the ROS framework of the theoretical aspects presented in the course will be addressed, and practical activities on designing and controlling mobile robots (TurtleBot3) and mobile manipulators (PAL Tiago and RB-KAIROS) will be carried out exploiting both simulation tools and real robots. At the end of the course students know basic mobile robotic technologies and they master modeling and control aspects of mobile robots used in both industrial and research settings.

Course contents

  • Introduction to mobile robotics.
  • Kinematic models and control aspects for mobile robots.
  • Task and trajectory planning for mobile robots.
  • Representation of Rotations.
  • Perception and sensing for mobile robots.
  • Navigation and localization.
  • Introduction to Reinforcement Learning.
  • Markov Decision Processes.
  • Dynamic Programming.
  • Monte Carlo Methods.
  • Temporal Difference Learning.
  • Value Function Approximation.
  • Simulation and control of robotic and mobile systems in the ROS environment.


  • Lecture Notes
  • Roland Siegwart, Illah R. Nourbakhsh, and Davide Scaramuzza. 2011. Introduction to Autonomous Mobile Robots (2nd. ed.). The MIT Press.
  • Sebastian Thrun, Wolfram Burgard, and Dieter Fox. 2005. Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). The MIT Press.
  • Sutton, Richard S and Andrew G Barto. Reinforcement learning: An introduction. MIT press, 2018.
  • Thomas M. Mitchell. 1997. Machine Learning (1st. ed.). McGraw-Hill, Inc., USA.
  • Stuart Russell and Peter Norvig. 2009. Artificial Intelligence: A Modern Approach (3rd. ed.). Prentice Hall Press, USA.

Teaching methods

The course consists of 60 hours in total, divided in frontal lectures and laboratory sessions with simulation software and experimental activities for the development of autonomous robotic solutions.

As concerns the teaching methods of this course unit, all students must attend Module 1, 2 on Health and Safety online

Assessment methods

Written exam regarding the topics presented during the lectures and oral discussion about an individual or a team project of an autonomous robot control system.

Teaching tools

Slides of the course, ROS, Python, Turtlebot 3, Tiago

Office hours

See the website of Gianluca Palli


Industry, innovation and infrastructure

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.