Materialien zur Vorlesung Mobile Roboter
Eine Sammlung der in der Vorlesung gezeigten Videos und Animationen:Localization
Monte Carlo Sampling with 1000 samples
The video shows the estimated distribution of a differential drive robot. The robot drives on the green trajectory and uses Monte Carlo Sampling with 1000 samples to estimate its pose.
Particle Filter with 100 samples
Again the robot follows the green trajectory. The robot is additionally equipped with a sensor which measures the distance and the angle to the green landmark. The odometry and the measurement is merged by a Particle Filter with 100 particles.
Particle Filter 100 samples with probabilities
The same example as above. The background visualizes the probability function of the measurement. It correlates to the weight a particle gets.
Navigation
Distance Transformation Algorithm - Dynamic Programming
- Search Space dimensionality: 2
- Motion Model: Omnidirectional
Distance Transformation Algorithm - Dynamic Programming - WORSTCASE
- Search Space dimensionality: 2
- Motion Model: Omnidirectional
Breadth-first Search
- Search Space dimensionality: 2
- Motion Model: Omnidirectional
Dijkstra
- Search Space dimensionality: 2
- Motion Model: Omnidirectional
Dijkstra
- Search Space dimensionality: 4
- Motion Model: Nonholonomic (car-like)
A* (using euclidean norm as heuristic)
- Search Space dimensionality: 2
- Motion Model: Omnidirectional
- Heuristic: Euclidean norm
A*
- Search Space dimensionality: 2
- Motion Model: Omnidirectional
- Heuristic: Chessboard distance
A*
- Search Space dimensionality: 4
- Motion Model: Nonholonomic (car-like)
- Heuristic: Euclidean distance
A*
- Search Space dimensionality: 4
- Motion Model: Nonholonomic (car-like)
- Heuristic: Optimal 2d heuristic, computed by dynamic programming in two dimensions
A* - Ignoring end orientation
- Search Space dimensionality: 4
- Motion Model: Nonholonomic (car-like)
- Heuristic: Euclidean distance