Competencies / Study results |
Code
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Study programme competences / results
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A1 |
CE01 - Capacidad para aplicar técnicas de análisis de datos y técnicas inteligentes en robótica y/o informática industrial |
A4 |
CE04 - Capacidad para uso y desarrollo de código y librerías que permitan captar el entorno y actuar sobre él en sistemas robóticos y/o industriales |
B2 |
CB7 - Que los estudiantes sepan aplicar los conocimientos adquiridos y su capacidad de resolución de problemas en entornos nuevos o poco conocidos dentro de contextos más amplios (o multidisciplinares) relacionados con su área de estudio |
B5 |
CB10 - Que los estudiantes posean las habilidades de aprendizaje que les permitan continuar estudiando de un modo que habrá de ser en gran medida autodirigido o autónomo. |
B9 |
CG4 - Extraer, interpretar y procesar información, procedente de diferentes fuentes, para su empleo en el estudio y análisis |
B10 |
CG5 - Capacidad para proponer nuevas soluciones en proyectos, productos o servicios |
B14 |
CG9 - Aplicar conocimientos de ciencias y tecnologías avanzadas a la práctica profesional o investigadora |
C1 |
CT01 - Adquirir la terminología y nomenclatura científico-técnica para exponer argumentos y fundamentar conclusiones |
C3 |
CT03 - Aplicar una metodología que fomente el aprendizaje y el trabajo autónomo |
Learning aims |
Learning outcomes |
Study programme competences / results |
Knowledge of the particularities of mobile robots in the context of industrial robotics, and in particular of autonomous mobile robots. |
AC1 AC4
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BC2 BC5 BC9 BC10 BC14
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CC1 CC3
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Knowledge of the physical principles of sensors used in autonomous robot navigation, and their application contexts. |
AC1 AC4
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BC9 BC14
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CC1 CC3
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Knowledge of the problems and main solutions in trajectory planning and autonomous navigation. |
AC1 AC4
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BC9 BC14
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CC1 CC3
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Knowledge of the main static and dynamic modelling techniques of the environment in which robots move. |
AC1 AC4
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BC9 BC14
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CC1 CC3
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Knowledge of the problems and the main existing solutions in localisation and mapping. |
AC1 AC4
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BC9 BC14
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CC1 CC3
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Contents |
Topic |
Sub-topic |
Introduction to mobile robotics |
Kinematics of mobile robots
Locomotion:
- Motors
- Degrees of freedom
- Legs
- Wheels
- Other effectors |
Perception in mobile robotics |
- Types of sensors
- Sensors in mobile robotics
-- Contact
-- Distance
-- Computer vision
-- IMU
-- GPS
- Control architectures
-- Deliberative
-- Reactive
-- Hybrid
-- Communications |
Movement control |
- Position control system |
Localization and mapping |
- Navigation:
-- Topological
-- Metric
- Simultaneous localisation and mapping
-- Localisation (odometry, beacons)
-- Visual SLAM |
Trajectory planning and navigation |
- Graph search
- Wavefront-based planning |
Planning |
Methodologies / tests |
Competencies / Results |
Teaching hours (in-person & virtual) |
Student’s personal work hours |
Total hours |
Guest lecture / keynote speech |
B5 B9 C1 C3 |
10.5 |
4.5 |
15 |
ICT practicals |
B2 B5 B9 B10 B14 C1 C3 |
10 |
10 |
20 |
Oral presentation |
A1 A4 B9 B10 B14 |
0.5 |
6.5 |
7 |
Supervised projects |
A1 A4 B2 B10 B14 C1 C3 |
0 |
30 |
30 |
|
Personalized attention |
|
3 |
0 |
3 |
|
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
Methodologies |
Methodologies |
Description |
Guest lecture / keynote speech |
Oral presentation of the theoretical syllabus by the teachers of the course. |
ICT practicals |
Face-to-face sessions with the computer in which teachers will explain the use and programming of the mobile robotics techniques seen in theory, so that students acquire sufficient skills to use them autonomously. Real and/or simulated robots will be used. |
Oral presentation |
Theory paper(s) on a topic proposed by the teachers of the course, which must be presented in front of classmates and also handed in in writing. |
Supervised projects |
Carrying out work/projects outside the classroom in which different programmes related to the topics seen in practical classes will be implemented through ICT, using real or simulated robots selected by the subject teachers. These projects will be carried out autonomously by the students and their progress will be supervised by the lecturers. |
Personalized attention |
Methodologies
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Supervised projects |
ICT practicals |
|
Description |
During the practical work through ICT, the student will be able to consult the teacher about all the doubts that may arise regarding the programming of the robots.
Tutored work: we recommend the use of personalised attention in these activities to resolve conceptual or procedural doubts that may arise during the resolution of practical problems. In addition, personalised attention will also focus on the student's explanation of the proposed solution.
Oral presentation: students will have to go to the teachers to resolve any doubts they may have about the preparation of the work to be presented, both in terms of the content and the presentation itself.
Students enrolled part-time will have an online personalised communication channel in all the methodologies. |
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Assessment |
Methodologies
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Competencies / Results |
Description
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Qualification
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Supervised projects |
A1 A4 B2 B10 B14 C1 C3 |
Several practical tasks will be proposed throughout the course focused on solving mobile robotics problems using real or simulated robots. These tasks will be developed autonomously by the student outside the classroom and must be defended in front of the lecturers. |
70 |
Oral presentation |
A1 A4 B9 B10 B14 |
The oral presentation of the theoretical work/works, the written version of the same and the active participation in the presentations of the classmates have an important weight in the final grade of the course. |
20 |
Guest lecture / keynote speech |
B5 B9 C1 C3 |
During the lectures, in person activities will be carried out to reinforce the comprehension of the theoretical aspects. |
10 |
|
Assessment comments |
In order to obtain a pass in this subject, a minimum mark of 50 must be obtained in all the above methodologies, with a minimum mark of 35 in the Tutored Work and 15 in the Oral Presentation. If the student does not pass the subject at the first sitting, he/she will have to repeat the necessary activities of the methodology/s that were not passed at the second sitting. As an example, if a student passed the Oral Presentation part but failed the Supervised Assignments, he/she will have to repeat the practical assignments necessary to achieve a pass, normally those that were not passed individually. Assessment of the extraordinary call: students who opt for this call will have to carry out the tutored work and oral presentation methodologies. Students must contact their teachers at the beginning of the term (January) in order to have enough time to submit their work. Students enrolled on a part-time basis must carry out the tutored work and oral presentation methodologies. In case of not being able to do the oral presentation with the rest of the students, they will have to arrange an alternative date with the professors in all the sessions. It is necessary to contact the lecturers at the beginning of the term (January) in order to have a sufficient deadline. In the case of plagiarism in internships or teaching assignments, article 11, section 4 b) of the UDC Student Disciplinary Regulations will be taken into account: b) Failure grade in the exam session in which the offence is committed and with respect to the subject in which it is committed: the student will be graded with a "fail" (numerical grade 0) in the corresponding exam session of the academic year, whether the offence is committed on the first or second occasion. To this end, the student's grade will be modified at the first opportunity, if necessary.
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Sources of information |
Basic
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• Siegwart, Roland (2004). Introduction to autonomous mobile robots. MIT Press
Nehmzow, Ulrich (2003). Mobile robotics a practical introduction. Springer
Kelly, Alonzo (2013). Mobile robotics: mathematics, models and methods. Cambridge University Press |
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Complementary
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Robin R. Murphy (2000). Introduction to AI Robotics. A Bradford Book
Joseph, Lentin (2015). Learning robotics using Python : design, simulate, program, and prototype an interactive autonomous mobile robot from scratch with the help of Python, ROS, and Open-CV. Packt Publishing
Lynch, Kevin (2017). Modern robotics : mechanics, planning, and control. Cambridge University Press |
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Recommendations |
Subjects that it is recommended to have taken before |
Autonomous Robotics Applications/770538015 | Machine Vision I/770538018 | Introduction to Python for Engineers/770538011 | Smart Robotics and Autonomous Systems/770538005 |
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Subjects that are recommended to be taken simultaneously |
Introduction to Python for Engineers/770538011 | Machine Learning I/770538016 |
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Subjects that continue the syllabus |
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Other comments |
-According to the different regulations applicable to university teaching, the gender perspective must be incorporated into this subject. -Work will be done to identify and modify sexist prejudices and attitudes and influence the environment to modify them and promote values of respect and equality. -Situations of gender discrimination should be detected and actions and measures should be proposed to correct them.
In order to help achieve a sustainable environment and fulfil the objective of the Green Campus Action Plan, the delivery of the documentary work carried out in this area: - Virtual format or digital support will be requested. - They'll be done on the Virtual Campus without printing them.
In case they’re done in paper: - Don't use plastics. - Use double-sided printing. - Use recycled paper. - Avoid printing drafts. |
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