## 5 Methodology for Anomaly Detection and Mitigation
### Mitigation
#### 5.2.2 Global Reconfiguration  
The objective of the global reconfiguration method is to find a sequence of configurations for each AGV of the fleet such that the overall fleet behavior is optimized. In contrast to the reconfiguration of AGV services (Section 5.2.1), all AGVs of the fleet are in general involved in a reconfiguration with this method. In this use-case, each (global) configuration is encoded as one bidding procedure that determines how the fleet distributes the transport tasks and when AGVs charge their batteries. This method can be used to support the mitigation of effects of anomalies, as in Sections 4.1 and 4.2, or as a fallback mitigation method if the reconfiguration of AGV services (Section 5.2.1) does not lead to a solution. The tackled reconfiguration problem in this use-case is related to Multi-Robot Task Assignment (MRTA) problems. In MPTA problems, the challenge is to decide which robot of a group of robots should take over which task in order to achieve the overall objectives of the group. The specific problems vary, among others, in the complexity of tasks, assumptions on knowledge on tasks, dependencies between robot schedules and capabilities of robots [57, 58]. However, MRTA solutions cannot be applied directly in this use-case, since here, the possible configurations of AGVs are already fixed and implemented and cannot be changed. The same holds for purely game-theoretical approaches, in which additionally extensive formal models are required that are not available in this use-case and would become too complex to be computationally feasible to handle. Nevertheless, game-theoretical ideas on decision making are taken over in this reconfiguration approach. For this, formal complex models are replaced by predictions and the usage of Digital Twins. For the rest of this section, it is assumed that it is not possible to make the misbehaving AGV operational again by a reconfiguration of AGV services (Section 5.2.1). This is the case when additional resources are needed, e.g., maintenance or repair staff when there was a AGV failure, as in Section 4.1, or a human worker that can clear the floor around the AGV if it is stuck between real obstacles, as in Section 4.3. The goal of the reconfiguration process is then to find a configuration that enables the remainder of the fleet to handle the predicted transport tasks without requiring human intervention at once. The detection of an anomaly triggers the four-step global reconfiguration: as a first step, a prediction of future transport task emergence is made. Data such as timestamps, start locations and destination locations of previous transport tasks are collected and serve as training data for learning methods for predictions of future tasks. A comparison of different learning methods applicable for this use-case can be found in [59]. The resulting list of transport tasks is further analyzed in the second step for the pre-selection of possible reconfiguration candidates. This step is performed to reduce the amount of required  
Figure 4: Service and communication setup after local reconfiguration is conducted (red: attacked service, green: services used by AGV1). computational power in the next step. Based on the predicted transport task emergence and the current AGV performance, it is checked which of the implemented configurations are applicable in the current situation. In the running example, anomaly detection points to one AGV as the origin of unexpected evolving KPIs. Hence, only configurations that exclude this AGV from the fleet are considered as reconfiguration candidates. Additionally, the pre-selection process can make use of the results of previous reconfiguration recommendations. If the current situation is similar to a previous situation, the previously recommended reconfiguration should be taken into account as the next reconfiguration. The set of previous solutions could be completed with other possible randomly chosen configurations to avoid the problem of repetitively neglecting promising reconfiguration alternatives.