## 2 Robot Control System  
### Zone-Based Segmentation of the Production Environment  
The general concept of zone-based navigation is inspired by zone management within an industrial production environment. The approach is based on virtual navigation zones [29]. Instead of using infrastructural markers, the zones can be defined in a given map by using 2D polygons. For each zone, a predefined robot behavior can be set with different parameter settings for maximum velocity, maximum acceleration, goal tolerance, warning indicators (visual and acoustic), or even different kinematic models reaching from the differential to holonomic models. For instance, Figure 3a shows a zone-based segmentation of a 60 \(\times\) 60 m production environment. The coloring represents the different cost levels of the zones ranging from green (minimum cost value) to red (maximum cost value). In Figure 3b, the zones are colored with an individual color for each zone. Figure 3c shows the corresponding layered costmap [31] configuration in hierarchical order. The ordering of the layers allows modulating the costs by overwriting them only when and where required. Figure 2: The AIMM OMNIVIL. The standard configuration of a layered costmap configuration includes a static, an obstacle, and an obstacle inflation layer. This default configuration does not fulfil the requirements of a dynamic production environment, including different manufacturing and transportation zones as well as a shared human-robot workspace. The implemented approach is based on the concept of layered costmaps described in [32], whereby each layer tracks the data to a specific functionality. With the cost value \(\Omega^{i}_{xy}=\{0,1,\ \dots,\ 254,\ 255\}\) for a cell \(c^{i}_{xy}\) at position \(P^{c}_{j}=\big{(}x_{j},y_{j}\big{)}\), the layer \(i\) contributes to the Master costmap as follows:  
The Static layer contributes to the master costmap by analyzing an a priori created 2D occupancy grid map \(G\). The layer provides information about free and occupied spaces in the production environment by Equation (1):  
\[\Omega^{1}_{xy}=\left\{ \begin{array}{ccc}0,&G(x,y)==Free\\ 254,&G(x,y)==Occupied\\ 255,&G(x,y)==NoInforation\end{array} \right. \tag{1}\]  
with _Free, Occupied_, and _NoInformation_ reflecting the related values of the occupancy grid map implementation. The Zone layers, Corridor (2), Restricted (3), Station (4), and Prohibition (5), contribute to the zone-based navigation layout of the production environment. The dimensions of the zones are provided in form of a polygon list. The corridor zone should be the preferred zone for the implemented path planner; therefore, the cost value of the corresponding cells is set to a small value of \(\Omega^{2}_{xy}=10\). The robot is allowed to enter restriction zones, but only if necessary. Therefore, the cost value for the corresponding cells is set to \(\Omega^{3}_{xy}=100\). The same condition applies to the station zones. Since a motion of the robot through a restriction zone is from higher preference than a motion through a station zone, the cost value of the corresponding cells is set to \(\Omega^{4}_{xy}=120\). The robot is not allowed to enter a prohibition zone; therefore, the cost value of the corresponding cells is set to \(\Omega^{5}_{xy}=250\). The Guard Rail layer is based on the inflation layer described in [32]. Originally, the inflation layer was designed to create a buffer zone around lethal obstacles to avoid the robot coming too close to the obstacles. We applied that method to define a buffer zone at the borders of the corridor zones. The robot uses these guard rail zones as a guide for navigating inside the corridor zones. The robot will navigate with a high preference alongside the edge between the guard rail zone and the corridor zone. The developed method is further explained in Section 2.2. The inflation radius is adjustable and defines the width of the guard rail zone.