| Study programme competencies |
| Code | Study programme competences |
| A21 | Coñecemento e aplicación dos principios fundamentais e técnicas básicas dos sistemas intelixentes e a súa aplicación práctica. |
| A42 | Capacidade para coñecer os fundamentos, paradigmas e técnicas propias dos sistemas intelixentes, e analizar, deseñar e construír sistemas, servizos e aplicacións informáticas que utilicen as ditas técnicas en calquera ámbito de aplicación. |
| A43 | Capacidade para adquirir, obter, formalizar e representar o coñecemento humano nunha forma computable para a resolución de problemas mediante un sistema informático en calquera ámbito de aplicación, particularmente os relacionados con aspectos de computación, percepción e actuación en ambientes ou contornos intelixentes. |
| A44 | Capacidade para desenvolver e avaliar sistemas interactivos e de presentación de información complexa e a súa aplicación á resolución de problemas de deseño de interacción persoa-computadora. |
| A45 | Capacidade para coñecer e desenvolver técnicas de aprendizaxe computacional e deseñar e implementar aplicacións e sistemas que as utilicen, incluídas as dedicadas á extracción automática de información e coñecemento a partir de grandes volumes de datos. |
| B1 | Capacidade de resolución de problemas |
| B3 | Capacidade de análise e síntese |
| B5 | Habilidades de xestión da información |
| B9 | Capacidade para xerar novas ideas (creatividade) |
| C2 | Dominar a expresión e a comprensión de forma oral e escrita dun idioma estranxeiro. |
| C4 | Desenvolverse para o exercicio dunha cidadanía aberta, culta, crítica, comprometida, democrática e solidaria, capaz de analizar a realidade, diagnosticar problemas, formular e implantar solucións baseadas no coñecemento e orientadas ao ben común. |
| C6 | Valorar criticamente o coñecemento, a tecnoloxía e a información dispoñible para resolver os problemas cos que deben enfrontarse. |
| C7 | Asumir como profesional e cidadán a importancia da aprendizaxe ao longo da vida. |
| C8 | Valorar a importancia que ten a investigación, a innovación e o desenvolvemento tecnolóxico no avance socioeconómico e cultural da sociedade. |
| Learning aims | |||
| Subject competencies (Learning outcomes) | Study programme competences | ||
| Know the problems to tackle when an autonomous robotic control system is developed | A21 A42 A45 |
B3 B5 |
C4 C6 C8 |
| Develop an autonomous control system for its operation in a real environment | A21 A43 A44 A45 |
B1 B3 B9 |
C4 C8 |
| Know the problems of knowledge representation in autonomous robotics | A43 |
B5 B9 |
C2 C6 C8 |
| Know the problems of sensing and actuation in systems that operate in the real world and real time | A42 A45 |
B1 B9 |
C2 C8 |
| Know the non-resolved problems in autonomous robotics | A21 A42 |
B5 B9 |
C2 C4 C6 C7 C8 |
| Contents |
| Topic | Sub-topic |
| Introduction to autonomous robotics | ¿What is an autonomous robot? History Sensors and actuators Behaviors Planning Learning and evolution |
| Elements of a robotic system | Robotic system Actuators and effectors Sensors Control architectures |
| Behavior-based robotics | Antecedents Classical control architectures Control architectures |
| Knowledge-based robotics | Knowledge Traditional deliberative robotics Navigation |
| Hybrid approximations | Main hybrid architectures Cognitive robotics |
| Evolutionary robotics | Evolutionary algorithms Application to robotics |
| Learning in autonomous robotics | Learning in classifier systems reinforcement learning: Q-learning Combination of reinforcement and connectionist learning |
| Planning | |||
| Methodologies / tests | Ordinary class hours | Student’s personal work hours | Total hours |
| Laboratory practice | 21 | 21 | 42 |
| Supervised projects | 0 | 30 | 30 |
| Oral presentation | 4 | 28 | 32 |
| Guest lecture / keynote speech | 21 | 21 | 42 |
| Personalized attention | 4 | 0 | 4 |
| (*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. | |||
| Methodologies |
| Methodologies | Description |
| Laboratory practice | Lab. sessions in which the teachers will explain the robotic platform and its development software in detail. Moreover, during these sessions, the students must perform the design, implementation and validation of the supervised projects under the supervision of a teacher. |
| Supervised projects | Programming exercises that must be developed using the selected robotic platform. These exercises will be carried out in an autonomous way and their progress will be supervised by the teachers |
| Oral presentation | Theoretical work about a specific topic from the contents that will be orally presented and discussed with other students |
| Guest lecture / keynote speech | Oral exposition by the teachers of the theory of the subject. |
| Personalized attention |
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| Assessment | ||
| Methodologies | Description | Qualification |
| Guest lecture / keynote speech | The attendance to the keynote speeches will be considered in the final mark | 5 |
| Laboratory practice | The attendance to the laboratory classes will be considered in the final mark | 5 |
| Supervised projects | Different programming projects will be proposed along the course that must be carried out in an autonomous way by the student and that will be presented and explained to the teachers afterwards. It is mandatory to pass this methodology independently in order to pass the whole subject. | 55 |
| Oral presentation | The oral presentation, the participation in the discussion and the written inform will be considered in the final mark. It is mandatory to pass this methodology independently in order to pass the whole subject. | 35 |
| Assessment comments | ||
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Evaluation of this course is based on independently overcoming the two main methodologies: supervised projects and oral presentation. The first one focuses on the practical demonstration of the knowledge and skills acquired to solve problems in autonomous robotics, and the second one in the completion and presentation of a paper on a specific topic within theoretical agenda. Thus, if the student does not pass the subject in the ordinary call, he / she shall repeat all activities that were not passed in the extraordinary call. As an example, if a student passed the oral presentation but failed the supervised projects, he / she shall repeat these. Students with part-time enrollment can displace the 5% of the qualification of the attendance to the other activities, both in theory and in practice, in case they can not regularly attend classes. This change in the qualification methodology shall be applied to teachers of the subject at the beginning of the course. |
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| Sources of information |
| Basic |
Bekey, A. (2005). Autonomous Robots. MIT Press
Arkin, R.C. (1998). Behavior Based Robotics. MIT Press
Santos, J., Duro, R.J. (2005). Evolución Artificial y Robótica Autónoma. RA-MA
Mataric, Maja J. (2007). The Robotics Primer. MIT Press |
| Complementary |
Santos, J. (2007). Vida Artificial. Realizaciones Computacionales. ServicioPublicaciones UDC
Floreano, D. and Mattiussi, C. (2008). Bio-Inspired Artificial Intelligence. Tema 7. MIT Press
Salido, J. (2009). Cibernética aplicada. Robots educativos. Ra-Ma
Nolfi, S., Floreano, D. (2000). Evolutionary Robotics. MIT Press
Thurn, S., Burgard, W., Fox, D. (2005). Probabilistic Robotics. MIT Press
Sutton, R.S., Burton A.G. (1998). Reinforcement Learning. MIT Press
Pfeifer, R. and Scheier, C. (1999). Understanding Intelligence. MIT Press |
| Recommendations | ||||
| Subjects that it is recommended to have taken before |
| Subjects that are recommended to be taken simultaneously |
| Subjects that continue the syllabus | ||||
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| Other comments | |