Predicting Conflict Zones on Terrestrial Routes of Automated Guided Vehicles with Fuzzy Querying on Apache Kafka

In today's world, smart factories are a coexisting element of smarticizing cities. Smart manufacturing of today relies on the automation of many component tasks of the production process. Automated guided vehicles (AGVs) that transport materials on the production lines are important elements of this automation. Appropriate management of a fleet of AGVs requires avoiding collisions. However, prediction and early detection of approaching collision points on the transportation routes not only prevent collisions but also enables adjusting the AGV operation and improving its flow. In this paper, we demonstrate the use of fuzzy sets and linguistic variables in collision prevention by processing AGV data streams with Apache Kafka. We extend the capabilities of Apache Kafka and ksqlDB towards fuzzy stream processing and use fuzzy KSQL queries to predict collisions. Our experiments prove that fuzzy querying against AGV data streams does not consume much time and computational resources, and we can successfully avoid collisions by predicting future positions of the AGV for various densities of data streams and widths of time windows.