Wednesday 11:00-12:30, KN:E-128 (K3), Lecturers:
Wednesday 12:45-14:15 KN:E-230 (taught primarily in Czech)
Wednesday 14:30-16:00 KN:E-230 (taught in English) Teachning assistants:
The course introduces a basic mobile robot structure design together with control methods aimed to achieve autonomous and collective behaviors for robots. Methods and tool s for data acquisition and processing are presented herein with the overall goal to resolve the task of autonomous navigation for mobile robots comprising the tasks of sensor fusion, environmental modeling including Simultaneous Localization And Mapping (SLAM) approaches. Besides sensor-processing related tasks, methods for robot trajectory planning will be introduced. The central topic of the course stands in specific usage of the afore methods capable of execution with groups of robots and taking the advantage of their cooperation and coordination in groups. Labs and seminars are organized in a form of an Open Laboratory whereas the students will resolve the given problem in simulated environments as well as with a real robot HW.
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Week | Topic | Slides |
---|---|---|
1. | Course introductions, Taxonomy of the localization problem, continuous localization. (MK) | Slides |
2. | Probabilistic methods of localization (MK) | Slides |
3. | ||
4. | SLAM. (MK) | Slides |
5. | Localization in multi-robot teams. (MK) | Slides |
6. | Kinematics of a mobile robot, trajectory control. (LP) | Slides |
7. | ||
8. | World models and their building. (LP) | Slides |
9. | Introduction to planning in mobile robotics. (LP) | Slides |
10. | Planning under uncertainty. (LP) | Slides |
11. | ||
12. | Probabilistic methods to planning (RRT). (LP) | Slides |
13. | Multi-robot systems, aspects of their design, cooperation, coordination, communication. Swarms. (MK) | Slides |
Week | Topic |
---|---|
1. | Labs organization, transformations |
2. | Iterative Closest Point (ICP) |
3. | Individual work (ICP) |
4. | |
5. | |
6. | |
7. | Kalman filter (KF+EKF) |
8. | Individual work (KF+EKF) |
9. | |
10. | Particle filter (PF) |
11. | Individual work (PF) |
12. | |
13. | |
14. | Individual work (PF), grading. |
See the separate page.