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.