450-4096/01 – Industrial Robotics II (PR II)
| Gurantor department | Department of Cybernetics and Biomedical Engineering | Credits | 4 |
| Subject guarantor | doc. Ing. Bohumil Horák, Ph.D. | Subject version guarantor | doc. Ing. Bohumil Horák, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Optional |
| Year | 2 | Semester | winter |
| | Study language | Czech |
| Year of introduction | 2019/2020 | Year of cancellation | |
| Intended for the faculties | FEI | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
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.
Teaching methods
Lectures
Individual consultations
Experimental work in labs
Project work
Other activities
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.
Compulsory literature:
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 literature:
Way of continuous check of knowledge in the course of semester
Continuous Study Control:
2 tests in the 5th and 10th weeks of the semester. Continuous realization of the semestral work.
Conditions for granting the credit:
Passing the tests, submitting the technical documentation and realizing the semestral project: 12-45 points.
Subject IR students evaluation
Maximum points achieved 100p
Minimum points achieved 51p
The final exam is written (1-30p, min.10p) and oral (1-25p, min.10p). The content of the exam is the knowledge studied by the student from lectures, practical exercises, information sources at http://lms.vsb.cz and compulsory literature.
The student is evaluated in the semester for attendance at practical exercises, two tests and the realization and presentation of the semestral work (SW) (1-45p, min.12p).
The evaluation of the work during the semester is as follows:
Test No.1 1-10p (min.5p)
Test No.2 1-10p (min.5p)
Participation in exercises 1-12p (min.10p)
Realization, technical documentation, presentation 0/1-13p (min.5p)
Note: The student, based on his / her request, is evaluated in exceptional and justified cases (in case of serious reasons and excused absences in exercises, lectures and excursions) in the form of a credit test.
E-learning
Other requirements
There are no additional requirements.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
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).
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks