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Intelligent Robotics

Code: PRODEI010     Acronym: RI

Classification Keyword
OFICIAL Intelligent Systems

Instance: 2018/2019 - 1S Ícone do Moodle

Active? Yes
Web Page: https://moodle.up.pt/course/view.php?id=3194
Responsible unit: Department of Electrical and Computer Engineering
Course/CS Responsible: Doctoral Program in Informatics Engineering

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
PRODEI 2 Syllabus 1 - 6 28 162

Teaching Staff - Responsibilities

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

Teaching - Hours

Recitations: 2,00
Type Teacher Classes Hour
Recitations Totals 1 2,00
Armando Jorge Miranda de Sousa 1,00
Luís Paulo Gonçalves dos Reis 1,00

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, ...


  •  Introduction

    • Artificial Intelligence

    • Basic concepts of Robotics

    • Artificial Intelligence in Robotics

    • History, Evolution, and Current Trends in Intelligent Robotic

  • Architectures for Robotic Agents

    • Reactive, Deliberative, Hybrid

    • Belief, Desire and Intentions (BDI)

    • Cooperative Architectures

  • Perception in robotics

    • Odometry, Rotation and Compass Sensors

    • Sensors commonly used in robotics including artificial vision and depth

    • Sensor Fusion Techniques

  • Localization and Mapping

    • Creation, representation and updating of World States.

    • Markov and Gaussian Localization

    • Grid and Monte-Carlo Localization

    • Mapping: Occupancy Grid and SLAM

    • World Exploration

  • Actuation and control in robotics

    • Locomotion modes, issues with kinematics and dynamics

    • Actuators and associated physical parameters

    • Robot locomotion and its simulation

  • Navigation

    • Algorithms of navigation in known/unknown environments

    • Voronoi Diagrams

    • A* and D* Algorithms

    • Cellular Decomposition

  • Cooperative Robotics

    • Introduction to the cooperation between robots for teamwork

    • Joint Intentions, TAEMS, Role-Based, Social Rules

    • Communication and Mutual Modeling

    • Locker-Room, Strategical Coordination, Partial Hierarchical

  • Applications

    • 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

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

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

Mandatory literature

Murphy, Robin R.; Introduction to AI robotics. ISBN: 0-262-13383-0
Thrun, Sebastian; Probabilistic robotics. ISBN: 0-262-20162-3
Choset, Howie 070; Principles of robot motion. ISBN: 0-262-03327-5
Russell, Stuart; Artificial intelligence. ISBN: 0-13-360124-2

Complementary Bibliography

Arkin, Ronald C.; Behavior-based robotics. ISBN: 0-262-01165-4
RoboCup Series (1999-2008)
Manuais dos Simuladores: Soccerserver, RoboCupRescue e Ciber-Rato
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



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


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

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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