Akademik Veri Yönetim Sistemi
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE, cilt.33, sa.5, ss.847-865, 2021 (SCI-Expanded)
Autonomous transfer vehicles (ATVs) can be considered as one of the critical components of context-aware structured smart factories in Industry 4.0 era. Conventional mapping methods such as grid maps can provide information for navigation, but they are not enough for complex environments that require interactions. On the other hand, high-definition (HD) mapping, which is mainly used in traffic networks, includes more information about an environment to perform excellent autonomous behaviour. In order to increase the efficiency of ATVs in flexible factories, an up-to-date environmental map information is required to perform successful long-term autonomous navigation. Therefore, when there exists a change in the environment, a simultaneous update of HD-map is as important as the creation of it. In this study, we propose an HD-map update methodology for ATVs that operates in smart factories. To the best of our knowledge, HD mapping has not been applied in smart factories. The proposed method includes the object detection and localisation tool to detect objects visually and determines their positions in connection with the conventional maps of the environment. Experimental results of a simulated factory environment demonstrate that the ATV can properly update the HD-map when a predefined sign is removed from or a new sign is added to the environment.