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.
Teachers
Person in charge
Albert Oller Pujol (
)
Weekly hours
Theory
1.2
Problems
0
Laboratory
0.6
Guided learning
0
Autonomous learning
3.2
Objectives
Probabilistic techniques applied in robotics
Related competences:
Subcompetences:
Bayesian filters, Extended Kalman filters, Ant colony optimization, Particle filtering
Search techniques are applied in robotics
Related competences:
Subcompetences:
Voronoi teselation, A*, C-space
Decision making techniques applied in robotics
Related competences:
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)
Evaluation methodology
Report of Probabilistic methods 33%
Report of Search methods 33%
Report of Decision Making methods 33%
Bibliography
Complementary:
Scientific papers will be provided -
Múltiple authors, ,
.