Modern logistic operations in warehouses consist of human and robots working in the same large indoor or outdoor areas. To design safe and efficient area and avoid collisions or accidents, localization and tracking of humans need to be deployed. The development of location based service, such as human activity monitoring, has been one of the drivers for the last decade interest in indoor or outdoor positioning and localization. In this work, a novel tracking system integrating inertial measurements and an ultra-wide band infrastructure is proposed to follow and localize humans in a warehouse scenario. The system has been designed to be self-configurable, able to learn online most of the needed parameters. Being the computational load low, it can be implemented on wearable devices. We tested the tracking system in a real outdoor scenario where the adaptive online algorithms have shown their effectiveness and improvements with respect to existing approaches.
Human adaptive tracking and localization in Logistic Operations / Pascucci, Federica; Dolfi, Marco; Morosi, Simone; Giarre, Laura. - (2023), pp. 1774-1779. (Intervento presentato al convegno 9th International Conference on Control, Decision and Information Technologies, CoDIT 2023 tenutosi a ROME nel july 03-06 2023) [10.1109/CoDIT58514.2023.10284400].
Human adaptive tracking and localization in Logistic Operations
Laura Giarre
Writing – Original Draft Preparation
2023
Abstract
Modern logistic operations in warehouses consist of human and robots working in the same large indoor or outdoor areas. To design safe and efficient area and avoid collisions or accidents, localization and tracking of humans need to be deployed. The development of location based service, such as human activity monitoring, has been one of the drivers for the last decade interest in indoor or outdoor positioning and localization. In this work, a novel tracking system integrating inertial measurements and an ultra-wide band infrastructure is proposed to follow and localize humans in a warehouse scenario. The system has been designed to be self-configurable, able to learn online most of the needed parameters. Being the computational load low, it can be implemented on wearable devices. We tested the tracking system in a real outdoor scenario where the adaptive online algorithms have shown their effectiveness and improvements with respect to existing approaches.
Pubblicazioni consigliate
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris
