TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Bitsch, Günter
A1  - Schweitzer, Felicia
T1  - Selection of optimal machine learning algorithm for autonomous guided vehicle’s control in a smart manufacturing environment
JF  - Procedia CIRP
N2  - Artificial intelligence is a field of research that is seen as a means of realization regarding digitalization and industry 4.0. It is considered as the critical technology needed to drive the future evolution of manufacturing systems. At the same time, autonomous guided vehicles (AGV) developed as an essential part due to the flexibility they contribute to the whole manufacturing process within manufacturing systems. However, there are still open challenges in the intelligent control of these vehicles on the factory floor. Especially when considering dynamic environments where resources should be controlled in such a way, that they can be adjusted to turbulences efficiently. Therefore, this paper aimed to develop a conceptual framework for addressing a catalog of criteria that considers several machine learning algorithms to find the optimal algorithm for the intelligent control of AGVs. By applying the developed framework, an algorithm is automatically selected that is most suitable for the current operation of the AGV in order to enable efficient control within the factory environment. In future work, this decision-making framework can be transferred to even more scenarios with multiple AGV systems, including internal communication along with AGV fleets. With this study, the automatic selection of the optimal machine learning algorithm for the AGV improves the performance in such a way, that computational power is distributed within a hybrid system linking the AGV and cloud storage in an efficient manner.
KW  - Artificial Intelligence
KW  - autonomous guided vehicles
KW  - industry 4.0
KW  - intelligent control
Y1  - 2022
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-36751
SN  - 2212-8271
SS  - 2212-8271
U6  - https://doi.org/10.1016/j.procir.2022.05.166
DO  - https://doi.org/10.1016/j.procir.2022.05.166
VL  - 107
IS  - Leading manufacturing systems transformation – Proceedings of the 55th CIRP Conference on Manufacturing Systems 2022
SP  - 1409
EP  - 1414
S1  - 6
PB  - Elsevier
CY  - Amsterdam
ER  -