Autor: Lutz Frommberger

So-called chaotic storages get increasingly important in commercial use. Their high dynamics and resulting uncertainty about storage levels result in high requirements on logistic processes. The project presented in this paper combines methods to meet these requirements by the use of an autonomous stocktaking robot. It uses approaches from the field of cognitive inspired Artificial Intelligence, enabling the robot to act purposefully in an unknown environment. Even if the environment is constantly changing, the robot is able to acquire robust information about the current state of e.g., storage areas, their position and goods stored in them. The information gathered is of coarse granularity but is still be a valuable basis for the analysis and optimisation of intra-logistic pro-cesses.

Rules play an important role in everyday human interaction. In road traffic the participants have to obey to the rules to guarantee smooth traffic flow and to avoid accidents. The participants in these situations are called agents. Artificial agents that act in this environment must be aware of these rules and must act accordingly. Typically, such rules are formulated in natural language and thus, contain qualitative terms like “from left” or “turn right”. These terms are only intricately to implement to navigate agents. In this article we describe a so-called qualitative approach to formalize natural language spatial regulations. With the demonstrator SailAway [1] we show how the formalizations can be applied with little computational effort for collision free navigation in the domain of vessel navigation.