OVERVIEW

The experimental aspect is fundamental in robotics, in which various theoretical skills (automatic controls, computer vision, software architectures, real-time programming, ...) are merged in concrete systems and mechatronic platforms. The course aims to provide students with a methodology to accomplish this fusion and bridge the gap between theory and practical implementation, through lectures, exercises, and projects.

AIMS AND CONTENT

LEARNING OUTCOMES

The course's aim is to put into action the theoretical knowledge acquired in other courses, providing some robotic setups for specific implementations. The course will also include methodological information on experiments design and validation of results.

AIMS AND LEARNING OUTCOMES

Active participation in the proposed training activities (lectures and laboratory activities), individual study and the realization of group projects will allow students to:

- know and learn how to use, in simulation and real contexts, software frameworks for robotics;

- know and learn how to use, in simulation and real contexts, tools for synchronous and asynchronous communication between processes;

- implement a robotic simulation, using software tools such as Gazebo and V-REP;

- create new robotic models and simulation control plugins, thus having complete control over the simulation environment;

- know, modify and use algorithms for navigating mobile robots in unstructured environments;

- implement simple controllers for robot manipulators in a simulation environment;

- use electronic hardware commonly used in robotics, such as Arduino, Raspberry, and NVIDIA Jetson;

- create a mobile robot, starting from a basic kit, able to interact with the surrounding environment, and perform simple tasks

PREREQUISITES

Since the main objective of the course is to practice theoretical aspects learned in other disciplines, the following knowledge is necessary to face the course optimally:

- software architectures for robotics

- ROS (Robot Operating System)

- automatic controls

- programming (C ++, python)

TEACHING METHODS

Teaching methods consist of:

- frontal lessons, offered asynchronously (short videos on the various aspects of the course program, uploaded on the Aulaweb platform);

- exercises and in-depth analysis of the aspects analyzed in the videos (online, via the TEAMS platform);

- group projects, in simulation or with real robots (in this second case, mixed groups will be created with students present on-site and "remote" students);

- virtual laboratories, offered through Docker containers;

- in-person meetings with small groups (for on-site students).

SYLLABUS/CONTENT

The course program consists of the following topics:

- Introduction to the course: experiments and challenges in scientific research;

- Fundamentals of ROS and Docker;

- Simulations with ROS, Gazebo, and VRep;

- Robotic models with ROS and Gazebo;

- Simulations of mobile robots;

- Simulations of manipulators;

- Use of Arduino, Raspberry, and Nvidia Jetson;

- Experiments with real robots.

RECOMMENDED READING/BIBLIOGRAPHY

All slides shown during the lessons and other teaching materials will be available on the Aulaweb platform. Generally speaking, notes taken during the lessons and teaching materials uploaded on Aulaweb will be sufficient for the course.

TEACHERS AND EXAM BOARD

Exam Board

CARMINE RECCHIUTO (President)

FULVIO MASTROGIOVANNI

LUCA BUONCOMPAGNI (President Substitute)

LESSONS

LESSONS START

21 September 2020

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The exam essentially consists of an oral test, which is a discussion about the projects carried out during the course. Indeed, the completion of 3 projects, which will be proposed during the course and will be carried out in a group or individually, is mandatory for the final exam. 

ASSESSMENT METHODS

The oral exam and the projects aim to ascertain the following aspects of the students' preparation:

- Acquired knowledge about the implementation of robotic simulations.

- Ability to apply correct methodologies for the practical solution of theoretical problems.

- Ability to adopt software architectures suitable for solving robotic problems.

- Ability to transport theoretical concepts to real robots.

Exam schedule