Course details
Robotics
ROBa Acad. year 2017/2018 Winter semester 5 credits
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
Language of instruction
Completion
Time span
Assessment points
- 55 pts final exam (written part)
- 20 pts mid-term test (written part)
- 25 pts projects
Department
Subject specific 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.
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.
Prerequisite knowledge and skills
There are no prerequisites
Syllabus of seminars
- 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.
- Models and control of the stationary robots.
- Effectors,sensors and power supply of robots. Applications of the cameras, laser distance meters, and sonars.
- 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.
Syllabus of laboratory exercises:
- Lego Mindstorms
- Basics of ROS, sensor reading
- Advanced work in ROS
Syllabus - others, projects and individual work of students:
Project implemented on some of the robots from FIT.
Progress assessment
Study evaluation is based on marks obtained for specified items. Minimimum number of marks to pass is 50.
Controlled instruction
- Mid-term written test.
- Evaluated project with a defence.
Course inclusion in study plans