Publications Repository - Gdańsk University of Technology

As the industrial sector is becoming ever more flexible in order to improve productivity, legacy interfaces for industrial applications must evolve to enhance efficiency and must adapt to achieve higher elasticity and reliability in harsh manufacturing environments. The localization of machines, sensors and workers inside the industrial premises is one of such interfaces used by many applications. Current localization-based systems are unable to deal with highly variable conditions, meaning that the solutions working well in stationary systems suffer from considerable difficulties in harsh environments, such as factories. As a result, the precision of localization techniques is not satisfactory in most industrial applications. This paper fills in the existing gap between static approaches and dynamic indoor positioning systems, by presenting a solution adapting the system to highly changeable conditions. The proposed solution makes use of a Machine Learning-based feedback loop that learns the variability of the environment. This feedback makes continuous fingerprint calibration feasible even in the presence of different machines and Industrial Internet of Things sensors that introduce variations to the electromagnetic environment. This paper also presents a comprehensive indoor positioning system solution that reduces complexity of hardware, meaning that a multi-standard-transceiver infrastructure may be adopted with reduced capital and operational expenditures.We have developed the system from scratch and have conducted an extensive range of testbed experiments showing that the multi-technology transceiver feature is capable of increasing positioning accuracy, as well as of introducing permanent fingerprints calibration at harsh industrial premises.

Authors

• Jordi Mongay Batalla,
• Constandinos X. Mavromoustakis,
• George Mastorakis,
• Neal Xiong, Naixue,
• prof. dr hab. inż. Józef Woźniak link open in new tab