Identifying Data

2013/14

Subject (*)

Robótica

Code

614G01098

Study programme

Grao en Enxeñaría Informática

Descriptors

Cycle

Period

Year

Type

Credits

Graduate

2nd four-month period

Fourth

Optativa

Language

Spanish

English

Prerequisites

Department

Computación

Coordinador

Santos Reyes, Jose

E-mail

jose.santos@udc.es

Lecturers

Becerra Permuy, Jose Antonio

Bellas Bouza, Francisco Javier

Santos Reyes, Jose

E-mail

jose.antonio.becerra.permuy@udc.es

francisco.bellas@udc.es

jose.santos@udc.es

Web

General description

Na materia de Robótica estúdanse os principais conceptos de robótica autónoma, facendo énfase no deseño automático de estratexias de control. Para iso, o contido da materia parte das estratexias clásicas de control para chegar ás máis actuais baseadas en conceptos da intelixencia computacional, tales como as redes neuronais, os algoritmos evolutivos e a aprendizaxe por reforzo.

Study programme competencies

Code	Study programme competences
A20	Coñecemento e aplicación dos principios fundamentais e técnicas básicas da programación paralela, concorrente, distribuída e de tempo real.
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.
B1	Capacidade de resolución de problemas
B2	Traballo en equipo
B3	Capacidade de análise e síntese
B6	Toma de decisións
B7	Preocupación pola calidade
B9	Capacidade para xerar novas ideas (creatividade)
C6	Valorar criticamente o coñecemento, a tecnoloxía e a información dispoñible para resolver os problemas cos que deben enfrontarse.
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	B1 B3 B9	C8
Develop an autonomous control system for its operation in a real environment	A21 A42 A43	B1 B2 B6 B7 B9	C8
Know the problems of knowledge representation in autonomous robotics	A43	B9	C8
Know the problems of sensing and actuation in systems that operate in the real world and real time	A20	B1 B2 B6 B7	C8
Know the non-resolved problems in autonomous robotics	A21 A42	B9	C6 C8

Contents

Topic	Sub-topic
Introduction to autonomous robotics	¿What is an autonomous robot? Classic control and cybernetics Artificial intelligence Bio-inspired robotics
Elements of a robotic system	Real environments Embodiment Sensors Actuators Autonomous robot control: - knowledge vs. behavior - reactive vs. deliberative
Knowledge-based robotics	Knowledge representation Modeling of the environment. Maps. Scheduling
Behavior-based robotics	Antecedents Reactive behaviours Implementation of behaviours.
Hybrid approximations	Deliberative and reactive Main hybrid architectures
Learning in autonomous robotics	Learning in classifier systems reinforcement learning: Q-learning Combination of reinforcement and connectionist learning
Evolutionary robotics	Evolutionary algorithms Main problems to solve Simulation vs. reality Hybrid approximations: evolution and learning
Multirobot systems	Coordination Composition of the team How to obtain the coordinated control

Planning

Methodologies / tests	Ordinary class hours	Student’s personal work hours	Total hours
Laboratory practice	21	21	42
Mixed objective/subjective test	3	18	21
Supervised projects	0	40	40
Guest lecture / keynote speech	21	21	42

Personalized attention	5	0	5

(*)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 design, implementation and validation of the control system of an autonomous robot in a real or simulated robot, under the supervision of a teacher.
Mixed objective/subjective test	Realization of objective tests about the theoretical contents of the subject
Supervised projects	Programming exercises that must be developed using a robotic simulator. These exercises will be carried out in an autonomous way and their progress will be supervised by the teachers
Guest lecture / keynote speech	Oral exposition by the teachers of the theory of the subject.

Personalized attention

Methodologies

Laboratory practice

Supervised projects

Description

During the lab practices and tutorials, the student can consult the teacher all the doubts that appear about the realization of the formulated practical problem or about the use of the simulator or the real robot.

Supervised projects: It is recommendable the use of a personal assistance in these activities to resolve conceptual doubts or procedures than can appear during the resolution of the practical problems. Also, the personal assistance will be focused on in the explanation, by the student, of the proposed solution.

Assessment

Methodologies	Description	Qualification
Laboratory practice	The weekly work of the student will be assessed, in the practical classes, by means of the evaluation of the progress of the weekly proposed exercixes	30
Mixed objective/subjective test	Objective test that will consist of an individual exam (written exam) about the theoretical contents of the subject. One or several tests could be performed depending on the course development.	50
Supervised projects	Different 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.	20

Assessment comments
The continuous supervision of the student's progress will have a 10% weight in the global qualification, distributed between the Laboratory Practice and the Supervised Projects

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

Sistemas Intelixentes/614G01020

Representación do Coñecemento e Razoamento Automático/614G01036

Desenvolvemento de Sistemas Intelixentes/614G01037

Aprendizaxe Automático/614G01038

Other comments

(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.