IT-MSC-2MIN-Compulsory-Elective - group I
Language of Instruction:English
This course is instructed in English, and it is intended for incoming Erasmus+ students too.

Completion:examination (written)
Type of
Hour/semLecturesSem. ExercisesLab. exercisesComp. exercisesOther
Guarantor:Orság Filip, Ing., Ph.D., DITS
Lecturer:Orság Filip, Ing., Ph.D., DITS
Rozman Jaroslav, Ing., Ph.D., DITS
Instructor:Kapinus Michal, Ing., DCGM
Faculty:Faculty of Information Technology BUT
Department:Department of Intelligent Systems FIT BUT
Learning objectives:
  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.
  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.
Learning outcomes and competences:
  The students acquire knowledge of current state and trends in robotics. Also, they acquire practical knowledge from construction and use of robots.
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.
Fundamental literature:
  • 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
  • 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
  • Sciavicco, L., Siciliano, B.: Modelling and Control of Robot Manipulators (Advanced Textbooks in Control and Signal Processing), Springer Verlag, 2000, ISBN 1852332212
  • Murphy, R., R.: An Introduction to AI Robotics (Intelligent Robotics and Autonomous Agents), Bradford Books, 2000,  ISBN 0262133830 
  • Laumond, J., P.: Planning Robot Motion, Springer-Verlag, 1998, ISBN 3540762191  
  • Spong, M., Vydyasagar, M.: Robot Dynamics and Control, J. Willey, 1989, ISBN 047161243X
Study literature:
  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
Progress assessment:
  1. Mid-term written test.
  2. Evaluated project with a defence.

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