## 2 Robot Control System
### Cost Adaption Based on Search Expansion Direction  
Common industrial navigation concepts are based on line following strategies [33] using passive [34] or active [35] landmark detection. These methods are approved in industrial environments, but not as flexible as desired. The well-known planning algorithms A\({}^{\star}\)[36] or Dijkstra [37] provide the most cost-efficient path available from a start position to a goal position, given a costmap. The presented navigation concept segments the production environment in zones of different cost levels. The resulting costmap is provided  
Figure 4: Human layer. (**a**) Occupancy grid map provided by the WMS; (**b**) costmap \(M\) with buffer zones around the human cells in blue and one exemplary visualized human safety zone (red circle); (**c**) costmap and occupancy grid map overlayed. to the path planner, resulting in a high preference for the corridor zone, without limiting the robot in the manner of a line follower. However, a line follower behavior is socially desirable for the corridor zone. This is particularly true for a shared human-robot production environment. In [38], the cell costs were slightly reduced for cells on the right side of a corridor. As a result, the pass speed of the human and the robot was significantly increased which indicates an optimized human-robot collaboration. The social behavior of right or left driving is widely used in public and industrial environments. Such a predictable and intuitive behavior of the robot is a key feature for beneficial human-robot cooperation. Instead of manipulating the costs before the actual planning process, our approach is applied while planning and takes the expansion direction of the planning algorithm into concern. As a result, the desired left or right driving behavior can be applied to any zone and in any direction. The aim is that the global planner prefers a path right next to the previously mentioned guard rail zone. This behavior is comparable to a line follower, without limiting the robot to a particular line motion. Figure 5 shows a horizontal and a vertical example scenario of a planning procedure in a grid-based costmap. The start cell for the planning procedure is set to the middle of the corridor zone. During the search for a valid path, a check is performed for each expanded cell of the corridor zone; if the cell is a neighbor of the guard rail zone, the cell is called a cell of interest. Each cell of interest can be either declared as a left-hand or right-hand driving cell. The cell type is determined by taking the expansion direction of the search algorithm into concern. The expansion direction can only be determined if a neighbored cell of interest exists. In Figure 5a, the cells at positions (7,4) and (9,8) are right-hand driving cells. The expansion direction is visualized with a red arrow. The cells at positions (9,4) and (7,8) are left-hand driving cells. The cells at (8,4) and (8,8) do not feature a specific type, since they had no neighbored cell of interest when they were expanded. In Figure 5b, the cells at (5,7) and (11,5) are right-hand driving cells. The cells at (5,5) and (11,7) are left-hand driving cells, and the cells at (5,6) and (11,6) of no type. During search expansion the path potential is stored in form of a grid-based potential map \(\Phi\). The potential map \(\Phi\) holds the information for each expanded cell of how much it costs to reach that cell from the start cell. Cells that are not expanded yet, are set to a clearly identifiable default value \(\varphi\). Algorithm 1 shows our approach applied to realize a right-hand driving behavior. Figure 5: Exemplary planning scenario. (**a**) Horizontal; (**b**) vertical. In this example, the right-hand driving behavior is applied to the corridor zone.