OVERVIEW

The Research Track I Course aims at providing students with advanced concepts needed for developing software for robotics, performing their research and presenting their results. The course will help the students in learning and analyzing the most relevant instruments in these contexts, such as data visualization libraries, software documentation tools, notebooks for robotics, statistical tests.

Furthermore, students will work on a specific line of research, elaborating an analysis of the state of the art on a topic chosen from among those proposed. A part of the course will therefore be dedicated to some aspects related to the research methodology.

AIMS AND CONTENT

LEARNING OUTCOMES

The teaching unit aims to equip students with the foundational skills for software programming in robotics. It covers essential development tools (Docker, GitHub, Linux), basic programming in Python and C++, and introduces the Robot Operating System (ROS and ROS2) for building and managing robotic applications.

AIMS AND LEARNING OUTCOMES

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

- create some effective visuals for showing data

- use notebooks for controlling and managing robotic simulations

- write proper documentation for the developed code

- define a research hypothesis

- use the correct statistical instruments for validating their hypotheses

- search scientific resources

- perform a literature analysis

- communicate effectively in written and oral form, adapting their communication to the context, using sources and aids of various kinds 

PREREQUISITES

No specific prerequisites are needed. Some elements of programming may be helpful.

TEACHING METHODS

Teaching consists of lectures and practical exercises.

During lectures, examples illustrating the implementation of the various topics will be presented.

Class exercises will be performed individually or in groups.

Students who hold valid certificates relating to Specific Learning Difficulties (SLD), disabilities or other educational needs are invited to contact the lecturer and the school’s disability liaison officer at the start of the course to agree on any teaching arrangements which, whilst respecting the course objectives, take into account individual learning styles.

The contact details for the university’s disability liaison officer are available at the following link: https://unige.it/commissioni/comitatoperlinclusionedeglistudenticondisabilita.

SYLLABUS/CONTENT

The course program consists of the following topics:

- Data visualization and Matplotlib

- Jupyter Notebook

- Software Documentation, Doxygen and Sphinx

- Statistical tools for robotics engineering

- Elements of Research Methodology

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 lectures and the teaching materials uploaded to AulaWeb will be sufficient.

Students with valid certifications for Specific Learning Disorders (SLDs), disabilities or other educational needs are invited to contact the teacher and the School's contact person for disability at the beginning of teaching to agree on possible teaching arrangements that, while respecting the teaching objectives, take into account individual learning patterns. Contacts of the teacher and the School's disability contact person can be found at the following link Comitato di Ateneo per l’inclusione delle studentesse e degli studenti con disabilità o con DSA | UniGe | Università di Genova

TEACHERS AND EXAM BOARD

Exam Board

CARMINE TOMMASO RECCHIUTO (President)

RENATO UGO RAFFAELE ZACCARIA

ENRICO SIMETTI (President Substitute)

LESSONS

LESSONS START

https://easyacademy.unige.it/portalestudenti/index.php?view=easycourse&_lang=it&include=corso
 

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

During the final exam the student will discuss his/her Research Line (i.e., a "fake" scientific paper) carried out during the course. 

The final grade will be composed of the evaluation of the assignment (50%).and the related discussion (50%)

ASSESSMENT METHODS

With the assignment and the final exam, students will demonstrate that they are able to:

- use tools for data visualization

- write a correct software documentation

- design a notebook for a robotic simulation

- define research hypotheses and perform statistical analyses

- perform a literature analysis

- present their work

- discuss and motivate the work carried out and the choices made

FURTHER INFORMATION

Students with valid certifications for Specific Learning Disorders (SLD) may request accommodations for exams at least 7 days prior to the exam date by filling out the “accommodation request form” (available via online services at https://modulionline.unige.it/richiesta-adattamenti# no-back), which will be automatically forwarded by the system to the instructor in charge of the course and to the faculty liaison for students with disabilities and SLDs in their School/Department.

The student will receive a copy of their request.