The discipline of robotics is now extending its arms towards new applications in new environments, to meet new demands of a new society. Most of the success is motivated by the application of AI techniques.
In this course, some of such AI techniques will be analysed.
Profesorado
Responsable
Albert Oller Pujol (
)
Horas semanales
Teoría
1.2
Problemas
0
Laboratorio
0.6
Aprendizaje dirigido
0
Aprendizaje autónomo
3.2
Objetivos
Probabilistic techniques applied in robotics
Competencias relacionadas:
Subcompetences:
Bayesian filters, Extended Kalman filters, Ant colony optimization, Particle filtering
Search techniques are applied in robotics
Competencias relacionadas:
Subcompetences:
Voronoi teselation, A*, C-space
Decision making techniques applied in robotics
Competencias relacionadas:
Subcompetences:
For each AI methodology:
Week-1. Classroom slides and paper introduction (by teacher)
Week-2. Homework: paper reading
Week-3. Paper discussion in classroom
Week-4. Report writing
Week-5. Oral presentation. Next paper introduction (by teacher)
Método de evaluación
Report of Probabilistic methods 33%
Report of Search methods 33%
Report of Decision Making methods 33%
Bibliografía
Complementaria:
Scientific papers will be provided -
Múltiple authors, ,
.