## 4 Discussion
### Path Generation  
Since the method is based on the acquisition of image data, a digital image is required as the initial environment. The provision of such a map can be generated, e.g., by hand sketches, scans, e.g., by LIDAR but also by manual scanning. Since noise can often occur, especially when scanning the drivable surface (see Figure 25), a robust filter must be available to clean the environment. For example, if there are individual 1-pixel holes in the map, these are assumed to be holes and are considered topologically during path generation. It was shown in Figure 18 that a noisy map with a large number of holes can lead to small individual paths. It is therefore necessary to clean up the map beforehand using suitable filtering methods or to manually specify a clear layout.  
If suitable maps are available, the paths can be estimated fully automatically. In contrast to the alternative path algorithms, A* or Dijkstra applied on the whole environment, topological reduced paths with alternative routes can be considered by anchor point shrinking implicitly (see Figure 11). These additional paths can be used to avoid an obstacle or a hole on two sides. Since path generation is mainly determined by the traversable area, only the paths to each task change locally if additional obstacles occur also locally (see Figures 16 and 17). The paths to avoid new obstacles change only locally, resulting in robust routing and human-expectable behavior. For example, in A* on the whole environment, the path layout changes greatly when the position of the target or task also changes (see Figure 11). This can lead to unexpected behavior, causing the agent to fidgety implement the path changes.  
In path algorithms such as A* or Dijkstra the shortest paths are determined by cost functions and the paths are often placed near the corners of the traversable area (see Figure 11). However, since the position of the robots is subject to some uncertainty, they may cut off the possible non-traversable area. In a factory or health environment, this leads to visually confusing situations and boundary violations. Similarly, there is often insufficient space to correct inaccurate robot positions. In addition, trajectories from the A*-method can lead to unintuitive trajectories, such as when the robot crosses the lane from the left edge to the right edge, leading to a possible overlap with the trajectories of oncoming traffic. Path reduction and generation based on the skeletons result in centered paths so that the greatest possible distance from the boundary of the drivable area can be realized. This robust and expectable path generation can be used to train humans in factory areas as well as in care stations to behave in such a manner. When a robot needs to be bypassed, the staff or patient can move directly to the boundary of the traversable area. Since such behavior is also found in road traffic in the form of a drivable road in  
Figure 26: Generation of graphs in a real world use case: two maps, two marked images, two segmented skeletons and two path suggestions including clearance width.  
the middle and pedestrian paths at the boundary, this is a familiar and intuitive behavior. Similarly, the path with the largest distance to the boundary provides the opportunity to reposition deviating robots with sufficient distance. The main drawback of skeleton-based path simplification is that the paths are often not efficient. While A* and Dijkstra without restriction aim for the shortest possible paths, the paths from the anchor point shrinking show suboptimal paths in terms of path length. For example, if process time has to be optimized, path generation via A* on the whole environment would be the better choice.