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Subject: COLLABORATIVE ROBOTICS (A.A. 2024/2025)


Course year 3
Electrical, electronic, and industrial automation technologies (lesson)
  • TAF: Compulsory subjects, characteristic of the class SSD: ING-INF/04 CFU: 2
Unit Laboratorio di Robotica Collaborativa
Other Skills Required for Access to the Job Market (laboratory)
  • TAF: Various educational activities SSD: NN CFU: 4
Exam type oral
Evaluation final vote
Teaching language Italiano
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The course provides the knowledge about understanding and analyzing the methodologies and the technologies used in modern industrial applications of robotics and, in particular, collaborative robotics.

The course provides knowledge about modeling, planning and control of industrial and collaborative robots and of their applications in the industrial context.

Admission requirements

Knowledge and skills on matrix and differential calculus.
Knowledge and skills on control architectures.

Course contents

(2 CFU)
1. Introduction to robotics and collaborative robotics (Theory)
2. Basic concepts of robotics: links, joints, motions and workspace (Theory and Lab)
3. Introduction to the simulator of an industrial controller. (Lab)

(2 CFU)
4. Trajectory planning and motion control (Theory and Lab)
5. Design of a robotic cell and experimental validation on simulator (Lab)

(2 CFU)
6. Safety in collaborative robots (Theory)
7. Current legislation: ISO / TS 15066 (Theory and Lab)
8. Application notes on how to guarantee application safety (e.g.: sensors) (Theory and Lab)
9. Design of a collaborative robotic cell and experimental validation. (Lab)

Teaching methods

Language of the course: the course will be held in italian. Lessons and exercises: The course includes theoretical lessons in the classroom and exercises with the electronic calculator in the computer lab. The exercises include the design and implementation of the controller and its computer simulation. Attending at the lectures is not mandatory but it is strongly recommended. Methods for working students: working students who cannot attend classes must communicate this to the teacher to receive specific indications on the topics to be studied on the recommended textbooks and teaching materials. Office hours: By appointment requested by e-mail addressed to the teacher.

Assessment methods

The student is offered an exercise to perform before going to the exam by two weeks from the end of the course. The student will present himself with the solution that will be discussed during the exam, also through the preparation of a descriptive document. The discussion will be followed by an interview on the program carried out in class to verify the student's level of knowledge.

Learning outcomes

1) Knowledge and understanding skills
1.a) Understand and be able to describe the basic concepts of control architectures for industrial and collaborative robots
1.b) Understand and be able to describe the basic concepts of modeling of manipulators and collaborative robots
1.c) Understand and be able to describe the basic concepts of trajectory generation algorithms
1.d) Understand and be able to describe the basic concepts of motion control of robotic manipulators
1.e) Understand and be able to describe the basic concepts related to safety of collaborative cells.

2) Applied Knowledge and understanding skills:
2.a) Being able to choose and use an algorithm for trajectory generation
2.b) Being able to choose and use an algorithm for motion control
2.e) Design a collaborative robotic cell.

3) Judgment autonomy:
3.a) Verify its own level of knowledge and understanding of the concepts taught during the course thanks to the possibility of intervening during the lectures and to the possibility represented by the project.
3.b) Re-organize the learnt knowledge and implement its own critical and autonomous evaluation skill.

4) Communication skills
4.a) Express in a correct and logical way its knowledge, recognizing the requested topic and replying appropriately to the exam questions.
4.b) Use appropriate language and terminology

5) Comprehension skills:
5.a) Apply an effective methodology that allows to face and solve complex problems and capitalize the learnt notions to complete its study path.
5.b) Update the learnt knowledge in response to changes linked to the technological evolution.


- Slide del corso. Le slide vengono messe a disposizione su Teams e Moodle uno-due giorni prima della lezione corrispondente.

- Sciavicco, Siciliano, Villani, Oriolo, Robotica: Modellistica, Pianificazione e Controllo, Springer