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Code: EIC0071     Acronym: ROBO

Classification Keyword
OFICIAL Artificial Intelligence

Instance: 2014/2015 - 1S Ícone do Moodle

Active? Yes
Responsible unit: Department of Electrical and Computer Engineering
Course/CS Responsible: Master in Informatics and Computing Engineering

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
MIEIC 17 Syllabus since 2009/2010 5 - 6 56 162

Teaching Staff - Responsibilities

Teacher Responsibility
Armando Jorge Miranda de Sousa

Teaching - Hours

Recitations: 3,00
Type Teacher Classes Hour
Recitations Totals 1 3,00
Armando Jorge Miranda de Sousa 3,00

Teaching language



  • To understand the basic concepts of Robotics and the context of Artificial Intelligence in Robotics.

  • To study methods of perception and sensorial interpretation (emphasizing computer vision), which allow to create precise world estates and mobile robots’ control methods.

  • To study the methods which allow mobile robots to navigate in familiar or unfamiliar environments using Planning and Navigation algorithms.

  • To study the fundamentals of cooperative robotics and robots' teams construction.

  • To analyze the main national and international robotic competitions, the more realistic robot simulators and the more advanced robotic platforms available in the market.

  • Improve the ability to communicate regarding scientific and technical issues.

  • Improve healthy scientific approach.

Learning outcomes and competences

At the end of this Curricular Unit, students should be able to:

  • Define Robotic Autonomy
  • Define Intelligent Robotic System (IRS)
  • Explain relation of Artificial Intelligence (IA) and IRSs
  • List Applications for Robotic Systems
  • List and use classical Robotic Architectures
  • Know the current State of the Art in Robotics
  • Know frequently used sensors and actuators (in robotics)
  • Evaluate usage of vision systems compared to other sensors
  • Use methodologies from: Data Fusion, IA, data processing and vision processing in order to build perceptions of the world state
  • Know and use methods for Localization, Planning and Navigation in robotics
  • Know and use one or more robotic systems or simulators
  • Know and use cooperation techniques for several types of collaborative robotics

Working method


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

Experience in computer language programming is needed - any language, frequentely used languages include Java, C++ or Object Pascal, ...


  1. Introduction

    1. Artificial Intelligence

    2. Basic concepts of Robotics

    3. Artificial Intelligence in Robotics

    4. History, Evolution, and Current Trends in Intelligent Robotics

  2. Architectures for Robotic Agents

    1. Reactive, Deliberative, Hybrid

    2. Belief, Desire and Intentions (BDI)

    3. Cooperative Architectures

  3. Perception in robotics

    1. Odometry, Rotation and Compass Sensors

    2. Sensors commonly used in robotics including artificial vision and depth

    3. Sensor Fusion Techniques

  4.  Localization and Mapping

    1. Creation, representation and updating of World States.

    2. Markov and Gaussian Localization

    3. Grid and Monte-Carlo Localization

    4. Mapping: Occupancy Grid and SLAM

    5. World Exploration

  5. Actuation and control in robotics

    1. Locomotion modes, issues with kinematics and dynamics

    2. Actuators and associated physical parameters

    3. Robot locomotion simulation

  6. Navigation

    1. Algorithms of navigation in known/unknown environments

    2. Voronoi Diagrams

    3. A* and D* Algorithms

    4. Cellular Decomposition

  7. Cooperative Robotics

    1. Introduction to the cooperation between robots for teamwork

    2. Joint Intentions, TAEMS, Role-Based, Social Rules

    3. Communication and Mutual Modeling

    4. Locker-Room, Strategical Coordination, Partial Hierarchical

  8. Applications

    1. National and International Robotic Competitions: RoboCup, RoboOlympics, Fira Cup, DARPA Grand-Challenge, Portuguese Robotics Open, Autonomous driving, Micro-Mouse (Micro-Rato) and fire fighting Robots

    2. Robotic simulators: Soccerserver 2D and 3D, RoboCup Rescue, Virtual Rescue, Ciber-Mouse

    3. Robotic Platforms: MindStorms, ERS210A e ERS-7 (Sony Aibos): Hardware, Software Architectures and Robotic Programming Languages.

Mandatory literature

Howie Choset, Kevin M. Lynch, Seth Hutchinson, George Kantor, Wolfram Burgard, Lydia E. Kavraki, Sebastian Thrun ; Principles of Robot Motion : Theory, Algorithms, and Implementations , Bradford Book, MIT Press, Cambridge, Massachussets, London England, 2005. ISBN: 0-262-03327-5
Robin R. Murphy; An Introduction to AI Robotics , Bradford Book, MIT Press, Cambridge, Massachussets, London England, 2000. ISBN: 0-262-13383-0
Russell, Stuart; Artificial intelligence. ISBN: 0-13-360124-2

Complementary Bibliography

Sebastian Thrun, Wolfram Burgard, Dieter Fox ; Probabilistic Robotics, MIT Press, Cambridge, Massachussets, London England, 2005. ISBN: 0-262-20162-3
Manuais dos Simuladores: Soccerserver, RoboCupRescue e Ciber-Rato
Manuais de OPEN-R e Documentação das Plataformas ERS210A e ERS7, 2005
Siciliano, Bruno; Khatib, Oussama (Eds.); Springer Handbook of Robotics, Springer, 2008. ISBN: 978-3-540-38219-5

Teaching methods and learning activities

  • Exposition with interaction in classes

  • Use of simulators for mobile robots navigation (Ciber Mouse - “ciber-rato”)

  • Assignments on cooperative robotics (“robosoccer” and “Robocup rescue”)

  • Exploration of mobile robotic platforms

  • Challenge students to higher level learning

  • Evaluation includes ability to search information, do scientific work, do technical work and disseminate the work done. Higher order thinking skills are encouraged

  • Detailed feedback given to students about the quality of their research work and learning process


Linguagem de Programação: C++
Simuladores Soccer-Server (2D e 3D)
Simulador Ciber-Rato
Simulador RoboCup Rescue


Technological sciences > Engineering > Knowledge engineering
Technological sciences > Engineering > Control engineering > Robótica Robotics
Technological sciences > Engineering > Simulation engineering
Technological sciences > Engineering > Computer engineering
Technological sciences > Technology > Knowledge technology > Agent technology

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation Weight (%)
Trabalho escrito 30,00
Trabalho laboratorial 70,00
Total: 100,00

Amount of time allocated to each course unit

Designation Time (hours)
Elaboração de projeto 40,00
Estudo autónomo 30,00
Frequência das aulas 42,00
Trabalho de investigação 20,00
Trabalho laboratorial 30,00
Total: 162,00

Eligibility for exams

  • Attendence.

  • Assignments with more than 6 out of 20 in each of them

Calculation formula of final grade

  • 10% HomeWorks

  • 20% Assignment 1 (includes oral presentation)

  • 20% Assignment 2 (includes scientific conference short paper)

  • 10% Assignment 3 (Half Way Project)

  • 40% Assignment 3 (Final Project), detailed as:

    • 10% Code & Functionalities & Demonstration

    • 10% "Conference" article

    • 05% references

    • 10% Presentation + Q&A

    • 05% video

Examinations or Special Assignments

  • HomeWorks

    • Small weekly assignments

  • Assignment 1

    • Research & Survey about New Trends in Robotics
      OR    Initial small project in intelligent robotics

  • Assignment 2

    • Simple a reactive robot

    • Frequently team of 2 students; individual works allowed; max team size of 4 students (Goals to be defined at the beginning of the work on a case to case basis - depending on team size, etc)

  • Course Project

    • Assignments 3 and 4 relate to "Course Project", the project in the field of the course

    • Assignment 3 - Half way evaluation of the status of the Course Project (design and implementation)

    • Assignment 4 - Demonstration of Course Project + Dissemination elements (Oral Presentation + Publishable Scientific Article + Video)

Internship work/project

Class Project: Ciber Mouse simulation agent (such as collaborative or mapping), autonomous driving or other scientific research project agreed by students and teacher

Special assessment (TE, DA, ...)

  • Attendance not required

  • 20% Assignment 1

  • 20% Assignment 2

  • 60% Assignment 4 - Project + Dissemination (Oral Presentation + Article + Video)

Classification improvement

  • Individual improvement of the previously presented works.

  • To improve homeworks, one must improve and present all homeworks

  • Improvements must be turned in until last exam season ("appeal" exam season)


Attention: Classes and course materials will be given to students in English.

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