Autonomous Systems

Code:

M.EEC033

Acronym:

SAUT

Keywords
Classification	Keyword
OFICIAL	Automation and Control

Instance: 2023/2024 - 1S

Active?	Yes
Web Page:	https://drive.google.com/drive/folders/11kkWq6K5Mt-TmIHwZkgd5kbovrp55eJd?usp=sharing
Responsible unit:	Department of Electrical and Computer Engineering
Course/CS Responsible:	Master in Electrical and Computer Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M.EEC	36	Syllabus	2	-	6	45,5

Teaching language

Suitable for English-speaking students

Objectives

Analysis, design and development of Autonomous Mobile Robots and its application in industry, services,
monitoring, surveillance, search and rescue, etc.

Learning outcomes and competences

- Use maps and estimate the localisation of mobile robots within those same maps.
- Control the navigation of mobile robots avoiding obstacles and including reactive and predictive behavior.
- Recognize objects through their shape, texture, volume or other patterns.
- Make decisions in an autonomous way, adapting and learning with the new situations.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Matriz operarions and calculus.
Kinematics and dynamics of robots.

Program

I. Estimation of the localisation of mobile robots.
II. Algorithms based on Kalman Filters with beacons, Kalman Filters with landmarks, map-matching,
Markov and Monte Carlo.
III. Introduction to Simultaneous Localization and Mapping (SLAM)
IV. High level control in mobile robots.
V. Reactive and predictive behaviour.
VI. Planning of trajectories and obstacle avoidance.
VII. Tasks assignment and scheduling
VIII. Recognition of shapes / patterns and their pose.
IX. Adaptability, learning and autonomy in robotic systems.

Mandatory literature

Roland Siegwart; Introduction to autonomous mobile robots. ISBN: 978-0-262-01535-6
Sebastian Thrun; Probabilistic robotics. ISBN: 0-262-20162-3

Teaching methods and learning activities

* The teaching of this course is developed in theoretical and laboratory lessons

* Lectures: formal theoretical basis exhibition, wherever possible with real application examples.

* Laboratory/practical: exploration of some existing software and implementation of practical work with real situations (work in group).

Evaluation Type

Evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	75,00
Teste	25,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	116,50
Frequência das aulas	45,50
Total:	162,00

Eligibility for exams

General rules applied at FEUP, presence and participation in practical classes, average of 7 values in the tests carried out related to the distributed evaluation.

Calculation formula of final grade

(Final exam)*0.75 + (average of tests taken in practical classes)*0.25

Classification improvement

In the grade improvement examination, only the final examination component can be improved. The distributed assessment component cannot be upgraded.