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Code: EIC0071     Acronym: ROBO

Classification Keyword
OFICIAL Artificial Intelligence

Instance: 2018/2019 - 1S Ícone do Moodle

Active? Yes
Web Page: https://moodle.up.pt/course/view.php?id=2101
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 27 Syllabus since 2009/2010 5 - 6 42 162

Teaching Staff - Responsibilities

Teacher Responsibility
Luís Paulo Gonçalves dos Reis
Armando Jorge Miranda de Sousa

Teaching - Hours

Recitations: 3,00
Type Teacher Classes Hour
Recitations Totals 1 3,00
Armando Jorge Miranda de Sousa 1,50
Luís Paulo Gonçalves dos Reis 1,50
Mais informaçõesLast updated on 2018-09-20.

Fields changed: Objectives, Resultados de aprendizagem e competências, Componentes de Avaliação e Ocupação, Programa, Trabalho de estágio/projeto, Métodos de ensino e atividades de aprendizagem

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 creating precise world estates and mobile robots’ localization 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 human-robot interaction, robot learning, cooperative robotics and robot teams construction.

  • To analyze the main national and international robotics 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.

  • Promote healthy scientific approach.

Learning outcomes and competences

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

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

Working method


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

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


  1. Introduction to Intelligent Robotics:
    Basic Concepts of Robotics, Artificial Intelligence in Robotics; Areas and Applications of Intelligent Robots; Perception-Decision-Action Loop; Architectures for Robotic Agents; Robotic Competitions; Simulation and Robotic Simulators; History, Evolution, and Current Trends in Intelligent Robotics.

  2. Robotics Middleware and ROS: 
    Middleware and Robotics Middleware; Robotics Middleware Projects; Introduction to ROS – Robot Operating System; ROS Architecture; ROS Console Commands; Creating ROS Packages; ROS C++ Client Library; ROS Simulation; Visualizations and User Interface Tools; ROS Bags.

  3. Perception and Sensorial Interpretation: 
    Types of Sensors for Mobile Robots; Proximity/Contact Sensors; Position/Movement Sensors; Robot Vision (Cameras, Depth Sensors, Digital Image, Color Spaces, Image Processing, Image Analysis; Robot Hearing; Uncertainty Analysis and Representation; Sensor Fusion Techniques.

  4. Locomotion and Action: 
    Actuators for Mobile Robots; Locomotion Modes and Mechanisms; Wheeled Mobile Robots; Legged Mobile Robots and Biped Walking; Robot Manipulators and its Control; Mobile Robots Kinematics and Motion Control; Simulation of Robot Locomotion and Action.

  5. Localization and Mapping: 
    Creation, Representation and Update of Maps and World States; Metric Maps and Topological Maps; Markov, Gaussian and Grid Localization; Kalman Filters and Extended Kalman Filters (EKF) Localization; Particle Filters and Monte-Carlo Localization; SLAM – Simultaneous Localization and Mapping; Methods for SLAM (EKF-SLAM, FastSLAM and Graph SLAM).

  6. Planning and Navigation: 
    Path Planning in Known/Unknown Environments; Cellular Decomposition; Visibility Graphs; Voronoi Diagrams; Search, Dijkstra, A* and D* Algorithms; Potential Field Method; Obstacle Avoidance; Navigation Architectures; World Exploration Methods; High-Level Planning.

  7. Human-Robot Interaction (HRI): 
    Basics of Human-Computer Interaction; Perception for HRI; Decision-Making for HRI; Action for HRI; Human-Robot Intelligent Cooperation.

  8. Robot Learning: 
    Introduction and Challenges in Robot Learning; Dimensionality Reduction; Supervised Robot Learning; Evolutionary Robot Learning; Reinforcement Learning for Robotics; Optimization and Metaheuristics for Robotics; Self-Supervised, Imitation Learning, Deep Learning for Robotics; Multi-Robot Learning.

  9. Cooperative Robotics and Human-Robot Teams: 
    Cooperation between Robots for Teamwork; Joint Intentions, TAEMS, Role-Based, Social Rules; Multi-Robot Formations; Multi-Robot Communication and Mutual Modeling; Locker-Room, Strategical Coordination, Setplays; Swarm Robotics; Human-Robot Teams.

  10. Robotics in the Future: 
    Artificial Intelligence and Robotics in the Future; Visions, Science Fiction and Reality; Advanced Projects in Robotics in Portugal, EU, Japan and USA; Asimov Laws and their Future; Robot Ethics, Robot Rights and Robotic Governance; Industrial, Personal, Ubiquos and Cloud Robots; Robotics Future Trends and Applications; The Singularity?

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
Siciliano, Bruno; Khatib, Oussama (Eds.); Springer Handbook of Robotics, Springer, 2008. ISBN: 978-3-540-38219-5
Jason M. O'Kane; A Gentle Introduction do ROS, Independently published, 2013. ISBN: 978-14-92143-23-9 (Free - https://www.cse.sc.edu/~jokane/agitr/)

Teaching methods and learning activities

  • Exposition with interaction in classes

  • Examples are taken from projects coordinated/developed by the lecturers. 

  • Use of simulators for mobile robots navigation and humanoid robots

  • Assignments on cooperative robotics

  • Exploration of mobile robotic platforms

  • Challenge students to higher level learning

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

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


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


Technological sciences > Engineering > Control engineering > Robótica Robotics
Technological sciences > Engineering > Simulation engineering
Technological sciences > Engineering > Computer engineering
Technological sciences > Engineering > Knowledge 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 1 and 2 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, robotic soccer, humanoid robots 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 work that must have been previously presented in the final course presentation.

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


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

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