SylabUZ
| Course name | Fundamentals of robotics |
| Course ID | 06.9-WE-AutP-FundRob015-Er |
| Faculty | Faculty of Computer Science, Electrical Engineering and Automatics |
| Field of study | WIEiA - oferta ERASMUS / Automatic Control and Robotics |
| Education profile | - |
| Level of studies | First-cycle Erasmus programme |
| Beginning semester | winter term 2018/2019 |
| Semester | 5 |
| ECTS credits to win | 5 |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
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| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Lecture | 30 | 2 | - | - | Exam |
| Laboratory | 30 | 2 | - | - | Credit with grade |
To provide fundamental knowledge in subject of analysis, control and motion planning for modern industrial robotic systems.
To develop skills in proper robot selection and evaluation for industrial process automation.
Modeling and simulation, Signals and dynamic systems, Control engineering
Introduction. Historical outline. Overview of robotic mechanical systems. Tasks performed by robots. Categories of manipulators and robots. Basic components of industrial robots. Grippers. A robot as part of a control system. Structures of manipulators and robots. Linear transformations. Rigid-body rotations.
Coordinate transformations and homogeneous coordinates. Degrees of freedom. .
Kinematics. Kinematic relationships of a manipulator. Link description. Link connections. Forward kinematics. Denavit-Hartenberg parameters. Inverse kinematics. Jacobians.
Dynamics. Joint-space dynamics. Euler-Lagrange equations. Equations of motion. Newton-Euler formalism. Dynamics of a rigid manipulator. Simulation of dynamics.
Trajectory generation. Trajectory planning in configuration space. Cartesian planning. Geometrical problems. Real-time trajectory generation. Trajectory planning using a dynamic model. Collision-free trajectory planning.
Robotic drives. Hydraulic drives. Pneumatic drives. Electric drives.
Robotic sensors. Processing information from sensors. Computer vision. Stereo-based reconstruction.
Applications of robots in industry. Welding applications. Spray painting applications. Assembly operations. Palletizing and material handling. Dispensing operations. Laboratory applications. Work cells.
Wheeled mobile robots. Forward and inverse kinematics of mobile robots. Perception: sensors, representation of uncertainty, feature extraction. Self-localization.
Other applications of robots. Humanoids. Entertainment robots. Medical robots. Exosceletons. Military and police robots.
Lecture. Laboratory exercises
| Outcome description | Outcome symbols | Methods of verification | The class form |
Lecture – the main condition to get a pass are sufficient marks in written or oral tests conducted at least once per semester.
Laboratory – the passing condition is to obtain positive marks from all laboratory exercises to be planned during the semester.
Calculation of the final grade: lecture 50% + laboratory 50%
Modified by dr hab. inż. Wojciech Paszke, prof. UZ (last modification: 01-05-2020 17:01)
