About the course
Many confuse the recordings with actual lectures per week. The online courses are from previous years and have the advantage that you can stop the lecture at any time and restart it at any slide later by choosing it from the slide listing displayed on the left of the recording.
I will repeat the outline of the course here, so you can check our progress throughout the semester.
I give you some material ahead of time. You should finish processing the corresponding videos when the corresponding homework set gets presented.
Syllabus
| Section | Topic | Video | Homework |
|---|---|---|---|
| Representation | |||
| How to represent the robot to simplify the planning task — configuration space. Ways to reduce the robot to a point. | Video 1 | HmSet 1 | |
| Direct Planning Methods | |||
| Planning of the complete path online — Bug algorithms | Video 2 | HmSet 2 | |
| Planning of the complete path online — Wavefront planner | Video 2 | HmSet 2 | |
| Planning Strategies | |||
| Complete Map Knowledge Offline construction of roadmaps — Visibility Graph | Video 2 | HmSet 1 | |
| Complete Map Knowledge Offline construction of roadmaps — Voronoi Graph | Video 2 | HmSet 1 | |
| Complete Map Knowledge Offline construction of roadmaps — Trapezoidal Cell Decomposition and Boustrophedon | Video 2 | HmSet 2, 3 | |
| Partial Map Knowledge Heuristic method based on Potential Field | Video 3 | HmSet 2, 3 | |
| No Map Knowledge Sampling-based method — Multiple Query Probabilistic Roadmap | Video 4 | HmSet 3 | |
| No Map Knowledge Sampling-based method — Single Query Probabilistic Roadmap | Video 5 | HmSet 3 | |
| Analysis of the expansiveness (connectivity) of a graph | Video 6 | HmSet 3 | |
| Parametrization of the PRM (number of nodes etc.) based on expansiveness | Video 6 | HmSet 3 | |
| No Map Knowledge Improvements of sampling-based methods — Obstacle-Based PRM, RRT | Video 7 | — | |
| Expansiveness of the Space (PRM parametrization) | |||
| Methods to improve connectivity of probabilistic roadmaps | Video 7 | — | |
| Fusion of Uncertain Data for Mapping | |||
| Gaussian · Linear Fusion with Kalman Filter | — | — | |
| Gaussian · Non-Linear Fusion with Extended Kalman Filter | — | — | |
| Gaussian · Highly Non-Linear Fusion with Unscented Kalman Filter | — | — | |
| Arbitrary Uncertainties Fusion with Bayesian and Particle Filter | — | — | |
| Simultaneous Localization and Mapping (SLAM) | — | — | |
🎥 How to use the recordings
The recordings are from previous years and are intended as supporting material. You can pause the lecture at any time and jump to individual slides using the slide listing.
📝 Homework workflow
Material is provided ahead of time. Students should complete the corresponding videos when the relevant homework set is presented.