Robotics in the Smart City
Code: 106964 ECTS Credits: 6| Degree | Type | Year |
|---|---|---|
| Management of Smart and Sustainable Cities | OP | 4 |
Contact
- Name:
- Carles Pedret Ferré
- Email:
- carles.pedret@uab.cat
Teaching groups languages
You can view this information at the end of this document.
Prerequisites
The subject does not formally require any pre-requisite.
Objectives and Contextualisation
The objectives of this subject are framed in the application of Robotics in the field of smart city. In particular, it is intended:
- Provide the student with an overview of the basic concepts of robotics, artificial intelligence and internet of thinks.
- Ask the student to reflect on the ethical, social and economic implications of the application of robots in the city and intelligent industry.
- Apply robotic solutions to various current problems in the city.
Learning Outcomes
- CM16 (Competence) Develop proposals for information gathering systems for their analysis and subsequent use in decision-making.
- KM21 (Knowledge) Relate the models of systems in the city and their monitoring and control processes.
- SM21 (Skill) Use data acquisition systems (e.g. sensors and RFID tags) and their processing as a tool for control (e.g. of instrumentation and robots) and decision-making.
Content
The contents of the subject are divided into the following blocks:
Block 1. Theoretical and technical fundamentals:
Classic views of robotics.
Automation and control systems.
Fundamentals of Artificial Intelligence and desitions making.
Robotics and the Internet of things.
Block 2. Ethical, social and economic implications:
Ethical issues that arise from the proliferation of robots in the industrial and urban environment.
Industrial Shift: employability in the context of ubiquous robotics and sustainability models.
Impact of artificial intelligence on urban mobility.
Block 3. Applications of robotics.
Case study 3. Industry 4.0 and robotics. Challenges for the insertion of the robot in industrial fields, systems of interaction and collaboration with interconnected, interactive and collaborative robots. Challenges for the insertion of the robot in urban environments.
Activities and Methodology
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Case study seminars | 12 | 0.48 | CM16, KM21, SM21, CM16 |
| Laboratory sessions | 12 | 0.48 | CM16, KM21, SM21, CM16 |
| Theory sessions | 26 | 1.04 | CM16, KM21, SM21, CM16 |
| Type: Supervised | |||
| Tutories | 5 | 0.2 | CM16, KM21, SM21, CM16 |
| Type: Autonomous | |||
| Preparation of oral presentations | 35 | 1.4 | CM16, KM21, SM21, CM16 |
| Preparation of reports in writing | 60 | 2.4 | CM16, KM21, SM21, CM16 |
The teaching methodology to be followed is oriented to the student's learning of the subject on an ongoing basis. This process is based on the realization of three types of activities that will be developed throughout the course: theory lectures, case studies and laboratory practicum.
- Theory lectures: the teacher will provide information on the knowledge of the subject and on strategies to acquire, expand and organize this knowledge. The active participation of the students during these sessions will be encouraged, for example by raising discussions in those points that have a higher conceptual load.
- Case studies: students will have to actively participate to consolidate the knowledge acquired by solving, presenting and discussing problems and case studies. The transversal competences T01, T03 and T05 will be evaluated in the papers presented in the case studies by carrying out a critical analysis of the work done by each member of the team and the total work presented. This part will have a 5% to the grade of each of the case studies.
- Laboratory practicum. Students will work as a group in the development of laboratory practicum related to the use of computer and robotic tools.
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
Continous Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Laboratory reports | 30% | 0 | 0 | CM16, KM21, SM21 |
| Oral Presentations | 50% | 0 | 0 | CM16, KM21, SM21 |
| Reports from case studies | 20% | 0 | 0 | CM16, KM21, SM21 |
The evaluation of the subject will be done progressively and continuously throughout the semester for each block.
a) Blocks evaluation process.
For each of the three blocks that make the subject, the teacher will propose a series of activities (case studies and practices with a computer). The student, conducted by the teacher, will have to work on the development of the activities. Likewise, they must present them in the form and on the date indicated by the teacher and make an oral defense.
The final mark will be calculated as follows:
FinalMark = 40% * MarkBlock1 + 40% * MarkBlock2 + 20% * MarkBlock3
To pass the course, you must obtain a minimum grade of 5 as a result of the calculation of final grade (FinalMark). To apply the FinalMark formula it will be necessary to obtain a minimum grade of 3.5 in all grades of the blocks, i.e. each of the grades MarkBlock1, MarkBlock2 and MarkBlock3 must be greater than or equal to 3.5. Therefore, if a MarkBlock is graded with a grade lower than the one indicated above, the subject cannot be passed.
If a student does not reach the minimum grade of 3.5 in any of the MarkBlock and for this reason does not pass the course, the final grade will be a maximum of 4.5, i.e. it will be equal to the value of the weighted average if FinalMark is less than 4.5 or 4.5 if it is higher.
b) Programming evaluation activities
The scheduling of the evaluation activities will be given on the first day of the subject and will be made public through the Virtual Campus (Moodle) and on the website of the Engineering School, in the exams section.
c) Make-up process
This course is continuously assessed by means of the presentation of the works that correspond to the study cases and to the practices of laboratory. Papers must be submitted on a date and form according to the instructions provided by the teacher, who may request a resubmission as a form of make up. Since one MarkBlock can be offset with another, there are no recovery processes at the end of the semester.
d) Procedure for review of qualifications
For each evaluation activity, a place, date and time of revision in which the student can review the activity with the teacher will be indicated. In this context, claims may be made on the activity grade, which will be evaluated by the faculty responsible for the subject. If the student does not show up for this revision, this activity will not be reviewed later.
e) Qualifications
The final grade of the subject will be calculated according to the percentages mentioned in section a)of this point. Keep in mind that:
- Honor plates. Granting a grade of honor registration is only the decision of the faculty responsible for the subject. The regulations of the UAB indicate that MH can only be granted to students who have obtained a final grade equal to or greater than 9.00.
- Not evaluable. A student who has not submitted to any Activity will be considered "non-evaluable". In any other case, the evaluation criteria detailed above are followed.
f) Irregularities by the student, copy and plagiarism
Without prejudice to other disciplinary measures deemed appropriate, the irregularities committed by the student that may lead to a variation of the grade of an evaluation act will be scored with a zero. Therefore, copying, plagiarism, cheating, letting copy, etc. in any of the evaluation activities will involve suspending it with a zero. If it is necessary to pass any of these evaluation activities to pass the subject, this subject will be suspended directly, without the opportunity to recover it in the same course.
g) Evaluation of repeating students
No mark is saved from one course for the next. Repeating students follow the same evaluation standards asany other student.
h) Use of Artificial Intelligence tools
Prohibited use: In this course, the use of Artificial Intelligence (AI) technologies is not allowed at any stage. Any work that includes AI-generated content will be considered a breach of academic integrity and may result in a partial or total penalty in the activity’s grade, or more severe sanctions in serious cases.
Bibliography
Platform used for communication with students: Moodle.
Basic bibliography:
- A. Barrientos. Foundations of Robotics, McGraw-Hill, 2007.
- J. R. Mercader Uguina. The future of work in the era of digitization and robotics, Tirant Lo Blanch, 2017.
- International Journal of Social Robotics, Springer-Verlag, online magazine.
- Concha Bielza, A. Mateos and S. Ríos, Fundamentals of Decision Aid Systems, Ed. Ra-Ma, 2002.
- VV.AA. Artificial intelligence A Modern Approach, Prentice-Hall, 1996.
- T.S. Kuhn. The structure of Scientific Revolutions: 50th Aniversary Edition. The University of Chicago Press. 2012
Software
In Block 1 (Theoretical and technical fundamentals): Node-Red and Arduino
Groups and Languages
Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.