Course detail

Robotics (in English)

FIT-ROBaAcad. year: 2020/2021

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.

Guarantor

Ing. Jaroslav Rozman, Ph.D.

Department

Department of Intelligent Systems (UITS)

Offered to foreign students

Of all faculties

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

Not applicable.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Siegwart, R. a Nourbakhsh, I. R.: Introduction to Autonomous Mobile Robots. MIT Press, 2011. ISBN-13: 978-0262015356 (EN)
Thrun, S., Burgard, W. a Fox, D.: Probabilistic Robotics. MIT Press, 2005. ISBN 0-262-201623
Choset, H., Lynch, K. M., Hutchinson, S. et al.: Principles of Robot Motion. MIT, Press, 2005. ISBN 0-262-03327-5.
LYNCH, Kevin M. and PARK, Frank C. Modern robotics: mechanics, planning, and control. Cambridge (Reino Unido) : Cambridge University Press, 2018. (EN)
GOVERS, Francis X. Artificial intelligence for robotics: buildintelligent robots that perform human tasks using AI techniques. Birmingham, UK : Packt Publishing Ltd, 2018. (EN)
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 (EN)
Holland, J., M.: Designing Autonomous Mobile Robots: Inside the Mind of an Intelligent Machine, 2003, ISBN 0750676833
Craig, J., J.: Introduction to Robotics: Mechanics and Control, Prentice Hall, 2003, ISBN 0201543613 (EN)
Sciavicco, L., Siciliano, B.: Modelling and Control of Robot Manipulators (Advanced Textbooks in Control and Signal Processing), Springer Verlag, 2000, ISBN 1852332212 (EN)
Murphy, R., R.: An Introduction to AI Robotics (Intelligent Robotics and Autonomous Agents), Bradford Books, 2000,  ISBN 0262133830 (EN)
Spong, M., Vydyasagar, M.: Robot Dynamics and Control, J. Willey, 1989, ISBN 047161243X

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

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

Language of instruction

English

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.

Classification of course in study plans

  • Programme IT-MGR-2 Master's

    branch MBI , any year of study, winter semester, 5 credits, elective
    branch MPV , any year of study, winter semester, 5 credits, elective
    branch MGM , any year of study, winter semester, 5 credits, elective

  • Programme IT-MGR-2 Master's

    branch MGMe , any year of study, winter semester, 5 credits, compulsory-optional

  • Programme IT-MGR-2 Master's

    branch MSK , any year of study, winter semester, 5 credits, elective
    branch MIS , any year of study, winter semester, 5 credits, elective
    branch MBS , any year of study, winter semester, 5 credits, elective
    branch MIN , any year of study, winter semester, 5 credits, compulsory-optional
    branch MMI , any year of study, winter semester, 5 credits, elective
    branch MMM , any year of study, winter semester, 5 credits, elective

  • Programme MITAI Master's

    specialization NADE , any year of study, winter semester, 5 credits, elective
    specialization NBIO , any year of study, winter semester, 5 credits, elective
    specialization NGRI , any year of study, winter semester, 5 credits, elective
    specialization NNET , any year of study, winter semester, 5 credits, elective
    specialization NVIZ , any year of study, winter semester, 5 credits, elective
    specialization NCPS , any year of study, winter semester, 5 credits, elective
    specialization NSEC , any year of study, winter semester, 5 credits, elective
    specialization NEMB , any year of study, winter semester, 5 credits, elective
    specialization NHPC , any year of study, winter semester, 5 credits, elective
    specialization NISD , any year of study, winter semester, 5 credits, elective
    specialization NIDE , any year of study, winter semester, 5 credits, compulsory
    specialization NISY , any year of study, winter semester, 5 credits, elective
    specialization NMAL , any year of study, winter semester, 5 credits, elective
    specialization NMAT , any year of study, winter semester, 5 credits, elective
    specialization NSEN , any year of study, winter semester, 5 credits, elective
    specialization NVER , any year of study, winter semester, 5 credits, elective
    specialization NSPE , any year of study, winter semester, 5 credits, elective

  • Programme IT-MGR-1H Master's

    branch MGH , any year of study, winter semester, 5 credits, recommended

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Ing. Jaroslav Rozman, 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. Effectors,sensors and power supply of robots. Applications of the cameras, laser distance meters, and sonars.
  5. Midterm test.
  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. Probability in robotics. Introduction. Bayesian filtering, Kalman and particle filters. Model of robot movements and measurement model.
  9. Methods of the global and local localization. GPS based localization, Monte Carlo method.
  10. Map building. Algorithms for simultaneous localization and mapping (SLAM).
  11. Trajectory planning in known and unknown environment. Bug algorithm, potential fields, visibility graphs and probabilistic methods.
  12. Introduction to control and regulation.
  13. Multicopters, principle, control, properties, usage. Human - robot collaboration.

Laboratory exercise

6 hours, compulsory

Teacher / Lecturer

Ing. Daniel Bambušek

Syllabus

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

Project

20 hours, compulsory

Teacher / Lecturer

Ing. Daniel Bambušek

Syllabus

Project implemented on some of the robots from FIT.