Course detail

Robotics

FIT-ROBAcad. year: 2015/2016

Basic components of the stationary industrial robots. Kinematic chains. Kinematics. Solution of the inverse kinematic task. Singularities. Dynamics. Equations of motion. Path planning. Robot control. Elements and structure of the mobile robots. Models and control of mobile robots. Sensoric systems of mobile robots. Localization and navigation. Environment maps. Man-machine interface, telepresence. AI in robotics. Microrobotics.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

Ing. Filip Orság, Ph.D.

Department

Department of Intelligent Systems (UITS)

Learning outcomes of the course unit

The students acquire knowledge of current state and trends in robotics. Also, they acquire practical knowledge from construction and use of robots.

Prerequisites

There are no prerequisites

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Study evaluation is based on marks obtained for specified items. Minimimum number of marks to pass is 50.

Course curriculum

Syllabus of lectures:
  1. History, evolution, and current trends in robotics. Introduction to robotics. Robotic applications. Robotic competitions.
  2. Kinematics and statics. Direct and inverse task of kinematics.
  3. Path planning in the cartesian coordinate system.
  4. Models and control of the stationary robots.
  5. Effectors,sensors and power supply of robots. Applications of the cameras, laser distance meters, and sonars.
  6. Basic parameters of the mobile robots. Model and control of the wheel mobile robots.
  7. Robotic middleware. Robot Operating System (ROS), philosophy of ROS, nodes and communication among them.
  8. Maps - configuration space and 3D models.
  9. Probability in robotics. Introduction. Bayesian filtering, Kalman and particle filters. Model of robot movements and measurement model.
  10. Methods of the global and local localization. GPS based localization, Monte Carlo method.
  11. Map building. Algorithms for simultaneous localization and mapping (SLAM).
  12. Trajectory planning in known and unknown environment. Bug algorithm, potential fields, visibility graphs and probabilistic methods.
  13. Introduction to control and regulation.

Syllabus of laboratory exercises:
  1. Lego Mindstorms
  2. Basics of ROS, sensor reading
  3. Advanced work in ROS

Syllabus - others, projects and individual work of students:
Project implemented on some of the robots from FIT.

Work placements

Not applicable.

Aims

To inform students about current state and future of robotics. Also, to inform students about peculiarities of robotic systems and prepare them for introduction of robotic systems to industry.

Specification of controlled education, way of implementation and compensation for absences

  1. Mid-term written test.
  2. Evaluated project with a defence.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

  1. Nolfi, S., Floreano, D.: Evolutionary Robotics : The Biology, Intelligence, and Technology of Self-Organizing Machines (Intelligent Robotics and Autonomous Agents), Bradford Books, 2004, ISBN 0262640562
  2. Holland, J., M.: Designing Autonomous Mobile Robots: Inside the Mind of an Intelligent Machine, 2003, ISBN 0750676833
  3. Craig, J., J.: Introduction to Robotics: Mechanics and Control, Prentice Hall, 2003, ISBN 0201543613
  4. Sciavicco, L., Siciliano, B.: Modelling and Control of Robot Manipulators (Advanced Textbooks in Control and Signal Processing), Springer Verlag, 2000, ISBN 1852332212
  5. Murphy, R., R.: An Introduction to AI Robotics (Intelligent Robotics and Autonomous Agents), Bradford Books, 2000,  ISBN 0262133830 
  6. Laumond, J., P.: Planning Robot Motion, Springer-Verlag, 1998, ISBN 3540762191  
  7. Spong, M., Vydyasagar, M.: Robot Dynamics and Control, J. Willey, 1989, ISBN 047161243X

Recommended reading

  1. Craig, J., J.: Introduction to Robotics: Mechanics and Control, Prentice Hall, 2003, ISBN 0201543613
  2. Murphy, R., R.: An Introduction to AI Robotics (Intelligent Robotics and Autonomous Agents), Bradford Books, 2000,  ISBN 0262133830
  3. Šolc, F.: Robotické systémy, VUT v Brně, 1990

Classification of course in study plans

  • Programme IT-MGR-2 Master's

    branch MBI , any year of study, winter semester, elective
    branch MPV , any year of study, winter semester, elective
    branch MGM , any year of study, winter semester, elective
    branch MSK , any year of study, winter semester, elective
    branch MIS , any year of study, winter semester, elective
    branch MBS , any year of study, winter semester, elective
    branch MIN , any year of study, winter semester, compulsory-optional
    branch MMI , any year of study, winter semester, elective
    branch MMM , any year of study, winter semester, elective

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Ing. Filip Orság, Ph.D.

Syllabus

  1. History, evolution, and current trends in robotics. Introduction to robotics. Robotic applications. Robotic competitions.
  2. Kinematics and statics. Direct and inverse task of kinematics.
  3. Path planning in the cartesian coordinate system.
  4. Models and control of the stationary robots.
  5. Effectors,sensors and power supply of robots. Applications of the cameras, laser distance meters, and sonars.
  6. Basic parameters of the mobile robots. Model and control of the wheel mobile robots.
  7. Robotic middleware. Robot Operating System (ROS), philosophy of ROS, nodes and communication among them.
  8. Maps - configuration space and 3D models.
  9. Probability in robotics. Introduction. Bayesian filtering, Kalman and particle filters. Model of robot movements and measurement model.
  10. Methods of the global and local localization. GPS based localization, Monte Carlo method.
  11. Map building. Algorithms for simultaneous localization and mapping (SLAM).
  12. Trajectory planning in known and unknown environment. Bug algorithm, potential fields, visibility graphs and probabilistic methods.
  13. Introduction to control and regulation.

Laboratory exercise

6 hours, optionally

Teacher / Lecturer

Ing. Filip Orság, Ph.D.

Syllabus

  1. Lego Mindstorms
  2. Basics of ROS, sensor reading
  3. Advanced work in ROS

Project

20 hours, optionally

Teacher / Lecturer

Ing. Filip Orság, Ph.D.