# 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.

### 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

• 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