Automated generation of roadmaps for automated guided vehicle systems

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Automated guided vehicle systems (AGVS) have become indispensable in advanced production facilities. Due to significant progress in the field of AGVS and the increased automation within production plants, the potential applications for AGVS increase. So far the roadmaps for the vehicles are mostly generated manually, which leads to long and laborious planning phases. This thesis examines how system planners' knowledge can be integrated into a pathfinding algorithm in order to combine human logic with mathematical optimization to generate roadmaps for AGVS that are both efficient and applicable. The combination of mathematical planning and human planning was achieved by combining a fuzzy inference system with a traditional pathfinding algorithm - the A* algorithm. The fuzzy inference system stores the knowledge of the system planners in the form of fuzzy rules and the output of the rules directly influence the path planning of the A* algorithm.