This paper describes data fusion methodologies for obstacle detection in an automation system based on advanced Automatic Guided Vehicles (AGV), used for automated logistics in modern factories. We present the background of the problem, introducing generic aspects of the system architecture designed to cope with the obstacle detection in automated factory logistics; then, we focus on the system specification for the module responsible of integrating data from different sources and providing a global representation of the environment. Finally, we present a comparative analysis among different strategies of multisensor data fusion compliant with the requirements of the described system, highlighting their advantages and drawbacks
Multisensor data fusion for obstacle detection in automated factory logistics / Cardarelli, Elena; Sabattini, Lorenzo; Secchi, Cristian; Fantuzzi, Cesare. - ELETTRONICO. - 1:(2014), pp. 221-226. (Intervento presentato al convegno 2014 10th IEEE International Conference on Intelligent Computer Communication and Processing, ICCP 2014 tenutosi a rou nel 4-6 Settembre 2014) [10.1109/ICCP.2014.6937000].
Multisensor data fusion for obstacle detection in automated factory logistics
CARDARELLI, ELENA;SABATTINI, Lorenzo;SECCHI, Cristian;FANTUZZI, Cesare
2014
Abstract
This paper describes data fusion methodologies for obstacle detection in an automation system based on advanced Automatic Guided Vehicles (AGV), used for automated logistics in modern factories. We present the background of the problem, introducing generic aspects of the system architecture designed to cope with the obstacle detection in automated factory logistics; then, we focus on the system specification for the module responsible of integrating data from different sources and providing a global representation of the environment. Finally, we present a comparative analysis among different strategies of multisensor data fusion compliant with the requirements of the described system, highlighting their advantages and drawbacksPubblicazioni 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