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ECTS Course Overview



Industrial Robotics II

* Exchange students do not have to consider this information when selecting suitable courses for an exchange stay.

Course Unit Code450-4096/02
Number of ECTS Credits Allocated4 ECTS credits
Type of Course Unit *Optional
Level of Course Unit *Second Cycle
Year of Study *
Semester when the Course Unit is deliveredWinter Semester
Mode of DeliveryFace-to-face
Language of InstructionCzech, English
Prerequisites and Co-Requisites Course succeeds to compulsory courses of previous semester
Name of Lecturer(s)Personal IDName
HOR02doc. Ing. Bohumil Horák, Ph.D.
Summary
The subject follows the Industrial Robotics I from the bachelor's study. It focuses on advanced robotics, sensors, mobile and collective robotics, machine learning and artificial intelligence. In the practical part of laboratory exercises it deals with the advanced control of industrial and mobile robots with more degrees of freedom with simple and multisensor systems for local and global monitoring of position, speed and acceleration in the area and space and autonomous fulfillment of given tasks.
Learning Outcomes of the Course Unit
The aim of the course is to provide students with extensive information in the field of industrial / mobile robots and its groups management, advanced robotics, mobile and collective robotics, machine learning and artificial intelligence. After studying the module, the student should get an overview of the AI methods used, the principles of machine learning, planning and decision making. They should gain practical experience with their use and acquire other skills needed to create intelligent agents and autonomous control of robot groups.
Course Contents
Lectures:
1. Introduction. Summary of the terms of robot technology.
2. Sensors for autonomous robotic systems. Local and global systems.
3. Mobile robot kinematics.
4. Localization problem. Continuous localization methods.
5. Computer vision. Image and its properties. Role of interpretation.
6. Representation of the robot world. Environment models.
7. Planning robot behavior. Autonomous systems.
8. Multi-robot systems. Aspects of the proposal. Cooperation. Coordination. Communication.
9. Localization in robot teams.
10. Navigate the robot to position. Guidance accuracy, space representation in robot memory, robot orientation, local and global systems.
11. Visual systems, orientation in space, representation of space in the robot flag database, link to the knowledge base.
12. Recognition / machine learning. Empirical evaluation of classifiers.

Labs:
Students will get acquainted with laboratory tasks, their operation, program tools and control programs. Modifying algorithms and verifying them will be the main output of the practical part of the studied subject. Students will use the knowledge gained in previous expanding courses focusing on industrial robotics, microcontrollers and computers, electronics and software development.

Laboratory Task 1: Robot with 1 DOF (position control, speed, acceleration, machine learning, pathway optimization, multisensor systems).
Laboratory 2: Robot with 2DOF (position control, speed, acceleration, machine learning, pathway optimization, multisensor systems).
Laboratory 3: Robot with 2 DOF (position control, speed, acceleration, motion trajectory, machine learning, pathway optimization, multisensor systems).
Laboratory Task 4: A group of mobile robot collaborators (position management, speed, acceleration, motion trajectory management, robot role / behavior, machine learning, pathway optimization, multisensor local and global systems, local and global control, strategic decision making, strategic control).
Recommended or Required Reading
Required Reading:
Reza N. Jazar: Theory of Applied Robotics: Kinematics, Dynamics, and Control. Springer, 2010.
Stuart J. Russel and Peter Norvig. Artificial Intelligence, a Modern Approach. 3rd edition, 2010
Reza N. Jazar: Theory of Applied Robotics: Kinematics, Dynamics, and Control. Springer, 2010.
Stuart J. Russel and Peter Norvig. Artificial Intelligence, a Modern Approach. 3rd edition, 2010
Recommended Reading:
Johnston, J.: The Allure of Machinic Life: Cybernetics, Artificial Life. The MIT Press, USA, 2008 ISBN 978-0-262-10126-4
Blach, T., Parker L.E.: Robot Teams - From Diversity to Polymorphism. AK Peters, Naick Massachusetts 2002
Johnston, J.: The Allure of Machinic Life: Cybernetics, Artificial Life. The MIT Press, USA, 2008 ISBN 978-0-262-10126-4
Blach, T., Parker L.E.: Robot Teams - From Diversity to Polymorphism. AK Peters, Naick Massachusetts 2002
Planned learning activities and teaching methods
Lectures, Individual consultations, Experimental work in labs, Project work, Other activities
Assesment methods and criteria
Tasks are not Defined