In this paper we introduce a novel cloud robotics architecture that provides different functionalities to support enhanced coordination of groups of Automated Guided Vehicles (AGVs) used for industrial logistics. In particular, we define a cooperative data fusion system that, gathering data from different sensing sources, provides a constantly updated global live view of the industrial environment, for coordinating the motion of the AGVs in an optimized manner. In fact, local sensing capabilities are complemented with global information, thus extending the field of view of each AGV. This knowledge extension allows to support a cooperative and flexible global route assignment and local path planning in order to avoid congestion zones, obstacles reported in the global live view map and deal with unexpected obstacles in the current path. The proposed methodology is validated in a real industrial environment, allowing an AGV to safely perform an obstacle avoidance procedure.
Cooperative cloud robotics architecture for the coordination of multi-AGV systems in industrial warehouses / Cardarelli, Elena; Digani, Valerio; Sabattini, Lorenzo; Secchi, Cristian; Fantuzzi, Cesare. - In: MECHATRONICS. - ISSN 0957-4158. - 45:(2017), pp. 1-13. [10.1016/j.mechatronics.2017.04.005]
Cooperative cloud robotics architecture for the coordination of multi-AGV systems in industrial warehouses
Cardarelli, Elena;Digani, Valerio;Sabattini, Lorenzo;Secchi, Cristian;Fantuzzi, Cesare
2017
Abstract
In this paper we introduce a novel cloud robotics architecture that provides different functionalities to support enhanced coordination of groups of Automated Guided Vehicles (AGVs) used for industrial logistics. In particular, we define a cooperative data fusion system that, gathering data from different sensing sources, provides a constantly updated global live view of the industrial environment, for coordinating the motion of the AGVs in an optimized manner. In fact, local sensing capabilities are complemented with global information, thus extending the field of view of each AGV. This knowledge extension allows to support a cooperative and flexible global route assignment and local path planning in order to avoid congestion zones, obstacles reported in the global live view map and deal with unexpected obstacles in the current path. The proposed methodology is validated in a real industrial environment, allowing an AGV to safely perform an obstacle avoidance procedure.File | Dimensione | Formato | |
---|---|---|---|
1-s2.0-S0957415817300521-main.pdf
Accesso riservato
Tipologia:
Versione pubblicata dall'editore
Dimensione
6.12 MB
Formato
Adobe PDF
|
6.12 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
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