Multi-robot systems are often static and pre-configured during the design time of their software. Emerging cooperation between unknown robots is still rare and limited. Such cooperation might be basic like sharing sensor data or complex like conjoined motion planning and acting. Robots should be able to detect other robots and their abilities during runtime. When cooperation seems to be possible and beneficial, it should be initiated autonomously. A centralized cloud control shall be avoided. Using software patterns belonging to service-oriented architectures, the robots are able to discover other robots and their abilities during runtime. These abilities are implemented as services and described by their interfaces. Composition of services can be done easily and flexibly. The software patterns originally belonging to cloud computing could be successfully adopted to decentralized multi-robot systems. The developed concept allows autonomous systems to cooperate flexibly and to compose multi-robot systems during runtime.

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Production systems are changing in many aspects on the way to a Factory of the Future, including the level of automation and communication between components. Besides all benefits, this evolution raises the amount, effect and type of anomalies and unforeseen behavior to a new level of complexity. Thus, new detection and mitigation concepts are required. Based on a use-case dealing with a distributed transportation system for production environments, this paper describes the different sources of possible anomalies with the same effect, anomaly detection methods and related mitigation techniques. Depending on the identified anomaly, the FoF should react accordingly, such as fleet or AGV reconfiguration, strong authentication and access control or a deletion of adversarial noises. In this paper, different types of mitigation actions are described that support the fleet in overcoming the effect of the anomaly or preventing them in the future. A concept to select the most appreciate mitigation method is presented, where the detection of the correct source of the anomaly is key. This paper shows how various techniques can work together to gain a holistic view on anomalies in the Factory of the Future for selecting the most appropriate mitigation technique.

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