At the end of the theoretical and practical teaching activities, the student will be able to operate in a Linux-based operating system in order to install and run the robot simulator Webots. Within Webots, the student will be able to design different types of control systems for mobile robotic platforms (e.g., the e-puck educational robot). The student will be capable to build both hand-coded controllers (e.g., probabilistic finite state machines) and those requiring the support of an optimisation algorithm to be parametrised (e.g., artificial neural networks synthesised using reinforcement learning). The student will know basic functionalities of ROS2 to be able to program mobile robotic platforms. The student will be also capable of porting onto a physical platform controllers developed with the simulator Webots. The students will also know basic theoretical concepts in bio-inspired robotics.
The objective of this module is to teach students how to design different types of control systems for autonomous robots using simulation environments such as Webots and ROS2.
The module is made of the following parts:
· Theoretical foundations of bio-inspired robotics, with a specific attention to swarm robotics systems
· How to install a Virtual Machine (VM) in Linux OS
· How to instal Webots simulator on a VM
· How to program Webots: i) to create a robot experimental scenario, and ii) to design robot controllers
· Introduction to ROS2
· Introduction to Multi-Agent Reinforcement Learning
· How to design Probabilistic Finite State Machines as robot's controller
· How to port control systems developed in simulation onto a physical robot
See description of module content
Practical sessions will be run to provide further support to assimilate theoretical and practical concepts of the content of this module. Practical session are scheduled one every two weeks of teaching. However, the frequency of practical sessions will be modulated (i.e., increased or decreased) based on students needs.
Given the highly practical content of this module, every lectures will be made of classic content illustration with the support of slides, and practical exercises aimed to allow the student to assimilate the lectures’ content. To realise the practical exercises, the student can either use the computational support provided by the Faculty, or use her own laptop. This latter option is highly encouraged since it will allow the student to practise outside the lectures.
The student will be evaluated with an assignment (made of more then one exercise) to be submitted few weeks after the end of teaching, and an oral exam in which the examiner will ask questions concerning the submitted assignment (e.g., questions on aspect concerning the implementation) and questions concerning the entire content of the module including the theoretical parts. The assignment will concern the design of a control systems for autonomous robots using the tools illustrated during the lectures. For her assignment, the student will be able to choose robot scenarios from a list of scenarios provided by the lecturer towards the end of the teaching. The assignment and the oral exam will count for 100% of the marks of this module. If the student fails the first session, she can submit the assignment and undergo the oral exam during the summer (second) session.
All reading material and any required support will be provided by the lecturer using the many freely available resources from the Web.
| Training | Study programme | Block | Credits | Mandatory |
|---|---|---|---|---|
| Master in Computer Science, Professional focus in Software Engineering | Standard | 0 | 5 | |
| Master in Computer Science, Professional focus in Data Science | Standard | 0 | 5 | |
| Master in Computer Science, Professional focus in Data Science | Standard | 2 | 5 | |
| Master in Computer Science, Professional focus in Software Engineering | Standard | 2 | 5 |