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
Department
Offered to foreign students
Learning outcomes of the course unit
Prerequisites
Co-requisites
Recommended optional programme components
Literature
John M. Holland: Designing Autonomous Mobile Robots: Inside the Mind of an Intelligent Machine, 2003, Newnes, ISBN-13 978-0750676830
Murphy, R., R.: An Introduction to AI Robotics (Intelligent Robotics and Autonomous Agents), Bradford Books, 2019, ISBN 9780262038485 (EN)
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
- Graded laboratories.
- Mid-term written test.
- Evaluated project with a defence.
Language of instruction
Work placements
Aims
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
Teacher / Lecturer
Syllabus
- History, evolution, and current trends in robotics. Introduction to robotics. Robotic applications. Robotic competitions.
- Kinematics and statics. Direct and inverse task of kinematics.
- Path planning in the cartesian coordinate system.
- Effectors,sensors and power supply of robots. Applications of the cameras, laser distance meters, and sonars.
- Midterm test.
- Basic parameters of the mobile robots. Model and control of the wheel mobile robots.
- Robotic middleware. Robot Operating System (ROS), philosophy of ROS, nodes and communication among them.
- Maps - configuration space and 3D models. Probability in robotics. Introduction. Bayesian filtering, Kalman and particle filters. Model of robot movements and measurement model.
- Methods of the global and local localization. GPS based localization, Monte Carlo method.
- Map building. Algorithms for simultaneous localization and mapping (SLAM).
- Trajectory planning in known and unknown environment. Bug algorithm, potential fields, visibility graphs and probabilistic methods.
- Introduction to control and regulation.
- Multicopters, principle, control, properties, usage. Human - robot collaboration.
Laboratory exercise
Teacher / Lecturer
Syllabus
- Lego Mindstorms
- Basics of ROS, sensor reading
- Advanced work in ROS
Project
Teacher / Lecturer
Syllabus