Logo

Intelligent Robots

Code: 106589
Credits: 6
2026/2027
Degree programme Type Course
Bachelor in Artificial Intelligence OB 3

Contact lecturer

Name :
Lluís Ribas Xirgo
Email :
lluis.ribas@uab.cat

Teaching staff

Carlos Garcia Calvo

Group languages

You can consult this information at the end of the document.

Prerequisites

For a full understanding of the contents of the subject, it is necessary to have basic skills in programming and a good mathematical foundation. For this, you must have passed Fundamentals of Programming II and Fundamentals of Mathematics I and II. You must also understand how computational systems are organized to carry out their functions and, for this, you must have taken Fundamentals of Computing.

Objectives

Robotics is the part of Engineering that applies to the development of robots, that is, machines with the ability to interact with their environment. The complexity of this interaction depends not only on the number of elements they include to act in their environment (actuators) but also on the information they can extract from the elements they use to perceive it (sensors).

Robots are intelligent depending on their ability to take advantage of information from their environment and their own experience to decide their future actions.

Depending on the actuators, a distinction can be made between manipulator robots (arms) and mobile robots (vehicles) whose development is different because they have equally different functionalities.

With this subject, students are expected to achieve the following objectives:

  • Learn about the use of service robots in industry and logistics.
  • Have notions of the development process of robot manipulators and robotic vehicles.
  • Acquire practical skills in the development of basic manipulators and mobile robots.
  • Knowing how to integrate robots into larger applications.

Learning outcomes

  • CM18 (Build prototypes of robotic and autonomous navigation systems specialised in specific tasks and environments.) Build prototypes of robotic and autonomous navigation systems specialised in specific tasks and environments.
  • KM35 (Describe the components and architecture of cyber-physical systems, including embedded system platforms, sensors, actuators, and real-time control devices.) Describe the components and architecture of cyber-physical systems, including embedded system platforms, sensors, actuators, and real-time control devices.
  • KM36 (Identify the algorithmic underpinnings of robotic and autonomous navigation systems related to kinematics and control systems.) Identify the algorithmic underpinnings of robotic and autonomous navigation systems related to kinematics and control systems.
  • SM43 (Use simulation tools and platforms for the development and validation of robotic and autonomous navigation systems.) Use simulation tools and platforms for the development and validation of robotic and autonomous navigation systems.
  • SM44 (Apply location, navigation, planning and control techniques and algorithms in the design and implementation of robotic system control software and autonomous navigation.) Apply location, navigation, planning and control techniques and algorithms in the design and implementation of robotic system control software and autonomous navigation.

Contents

  • Introduction to robotics
  • Kinematic models of robots
  • Robot control software design
  • Robot programming

Learning activities and methodology

Title Hours ECTS Learning outcomes
Theory: Study 22 0.88 KM35
Problems: Solving problems 24 0.96 KM36, SM43
Class: Active participation in the discussions arising from the presentation of content or the proposed solutions to problems 38 1.52 SM43, SM44
Tutoring: Follow-up of issues arising in class 2 0.08 CM18, KM35, KM36, SM43, SM44
Practices: Development of projects in the laboratory 12 0.48 CM18, SM44
Practices: Development of projects and preparation of reports 24 0.96 SM44
Practices: Monitoring the execution of laboratory projects 6 0.24 CM18

Teaching is structured around the following activities:

  • Classroom classes: Presentation of knowledge and discussion of solutions to problems both those proposed in the same classes and those that arose during the practice.
  • Laboratory practices: Teamwork sessions, all following a script and supervised by a teacher. Each session will cover a specific aspect of robot design and programming.
Annotation: within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

Assessment

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Make-up exam 50% 2 0.08 KM35, KM36, SM44
Midterm exam 25% 2 0.08 KM35, KM36, SM44
Final exam 25% 2 0.08 KM35, KM36, SM44
Laboratory (6) 25% 6 0.24 CM18, SM43
Continuous assessment assignments 25% 10 0.4 KM35, KM36, SM43

a) Procedure and assessment activities’ plan

The assessment is continuous with specific activities (exams and assignments) throughout the course. These assessment activities generate a series of grades that determine the final grade.

The calculation of the final grade n follows the following expression:

n = x·50% + p·25% + c·25%

where x is the grade for the exams; p, that for the laboratory project, and c, that for the continuous assessment.

If x < 5 or p < 5, the final grade n is, at most, a 4.5. In other words, the average of the exams and the project must be passed separately.

The exam grade (x) is the average of the midterm and final exam grades, or the final exam grade alone if it is higher. If the final exam grade is below 3.5, a resit exam must be taken.

Project grade p is obtained from the weighted average of the grades corresponding to each lab session. Six are planned. In case of non-attendance, the absent person will receive a 0 as the grade for the corresponding session.

Continuous assessment grade c is obtained from a weighted average of the problem-solving assignments completed throughout the course. There is no minimum and, therefore, the course can be passed with c = 0 as long as x·50% + p·25% ≥ 5.

b) Assessment activities schedule

The dates of all face-to-face activities, including assessment activities, and submission deadlines will be published on the virtual campus (CV) and may be subject to possible changes in programming for reasons of adaptation to possible incidents: they will always be previously informed through the CV since it is the usual mechanism for exchanging information between teachers and students outside the classroom.

In exceptional cases where the affected person receives approval for the rescheduling request of assessment activities (see “Exam Rescheduling” on the School’s website), an alternative will be offered that fits the course schedule.

c) Re-assessment procedure

In accordance with the coordination of the Degree and the deanship of the School of Engineering, the following activities are not recoverable:

- Project, 25% of the final grade

- Continuous assessment, 25% of the final grade

The average grade of the exams can be recovered with a specific make-up exam.

d) Assessment review procedure

Assessment activities can be reviewed any time after corresponding grades are published and before the deadline for the revision of the final exam.

Should the change of a grade be agreed upon, that grade may not be modified in a later review.

No reviews will be done after the closure of the reviews of the final exam, but for the make-up exam.

e) Grading

A “non-assessable” grade is assigned to students that have not participated in any assessment activity. In any other case, not participating in an assessment activity scores 0 in the weighted average computation.

Honors will be awarded to those who obtain grades greater than or equal to 9.0 in each part, up to 5% of those enrolled in descending order of final grades. They may also be granted in other cases if they do not exceed 5% and the final grade is equal to or greater than 9.0.

f) Irregularities, copies and plagiarism

Copies are evidence that the work or the examination has been done in part or in full without the author's intellectual contribution. This definition also includes attempts to copy in exams and reports, and violations of the norms that ensure intellectual authorship. Plagiarisms refer to the works and texts of other authors that are passed on as their own. They are a crime against intellectual property. To avoid plagiarism, quote the sources you use when writing the corresponding work reports or examinations.

In accordance with the UAB regulations, copies or plagiarisms or any attempt to alter the assessment result, for oneself or for others, like e.g. letting other copy, imply a final grade for the corresponding part (exam, continuous assessment or project) of 0 in the computation of the final score and failing the course. This does not limit the right to act against perpetrators, both in the academic field and in the criminal.

The use of Artificial Intelligence (AI) technologies as an integral part of the development of the work is permitted, provided that the result reflects a significant contribution by the student in the analysis and personal reflection. The student must clearly identify which parts have been generated with this technology, specify the tools used and include a critical reflection on how they have influenced the process and the result of the activity. The lack of transparency in the use of AI is considered a lack of academic honesty and entails a penalty in the grade of the activity, or greater sanctions in serious cases.

g) Assessment of repeaters

There is no differentiated treatment for repeaters, but they can take advantage of their own material from the previous year provided it is informed in the corresponding reports.

h) Single assessment

This course does not have a single assessment procedure.

Bibliography

  • Ribas-Xirgo, Ll. (2026). Simulator-Based Digital Twin of a Robotics Laboratory. Machines, 14(3), 273. https://doi.org/10.3390/machines140302273
  • Ribas-Xirgo, Ll. (2025). State Machine Model of the Operation Control of a Differential-Drive Mobile Robot. Preprints. https://doi.org/10.20944/preprints202511.0943.v1
  • Edward A. Lee and Sanjit A. Seshia. (2017). Introduction to Embedded Systems, A Cyber-Physical Systems Approach, Second Edition, MIT Press. https://ptolemy.berkeley.edu/
  • J.J. Graig (2005) Introduction to Robotics: Mechanics and Control. Pearson Education International.
  • R. Siegwart, I.R. Nourbaksh (2004) Introduction to Autonomous Mobile Robots. The MIT Press.

Software

  • CoppeliaSim, EDU Version, Coppelia Robotics [https://www.coppeliarobotics.com/]
  • ZeroBrane Studio, ZeroBrane [https://studio.zerobrane.com/]
  • Draw.io, diagrams.net [https://app.diagrams.net/]

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE

Type of teaching Group Language Semester Shift
(TE) Theory 71 English first semester afternoon
(PAUL) Classroom practices 711 English first semester afternoon
(PLAB) Practical laboratories 711 English first semester afternoon
(PLAB) Practical laboratories 712 English first semester afternoon