Worker monitoring and protection in collaborative robot (cobots) industrial environments requires advanced sensing capabilities and flexible solutions to monitor the movements of the operator in close proximity of moving robots. Collaborative robotics is an active research area where Internet of Things (IoT) and novel sensing technologies are expected to play a critical role. Considering that no single technology can currently solve the problem of continuous worker monitoring, the article targets the development of an IoT multisensor data fusion (MDF) platform. It is based on an edge-cloud architecture that supports the combination and transformation of multiple sensing technologies to enable the passive and anonymous detection of workers. Multidimensional data acquisition from different IoT sources, signal preprocessing, feature extraction, data distribution, and fusion, along with machine learning (ML) and computing methods are described. The proposed IoT platform also comprises a practical solution for data fusion and analytics. It is able to perform opportunistic and real-time perception of workers by fusing and analyzing radio signals obtained from several interconnected IoT components, namely, a multiantenna WiFi installation (2.45 GHz), a sub-THz imaging camera (100 GHz), a network of radars (122 GHz) and infrared sensors (813). The performance of the proposed IoT platform is validated through real use case scenarios inside a pilot industrial plant in which protective humanrobot distance must be guaranteed considering latency and detection uncertainties.
A Multisensory Edge-Cloud Platform for Opportunistic Radio Sensing in Cobot Environments
Manuel Beschi;
2021-01-01
Abstract
Worker monitoring and protection in collaborative robot (cobots) industrial environments requires advanced sensing capabilities and flexible solutions to monitor the movements of the operator in close proximity of moving robots. Collaborative robotics is an active research area where Internet of Things (IoT) and novel sensing technologies are expected to play a critical role. Considering that no single technology can currently solve the problem of continuous worker monitoring, the article targets the development of an IoT multisensor data fusion (MDF) platform. It is based on an edge-cloud architecture that supports the combination and transformation of multiple sensing technologies to enable the passive and anonymous detection of workers. Multidimensional data acquisition from different IoT sources, signal preprocessing, feature extraction, data distribution, and fusion, along with machine learning (ML) and computing methods are described. The proposed IoT platform also comprises a practical solution for data fusion and analytics. It is able to perform opportunistic and real-time perception of workers by fusing and analyzing radio signals obtained from several interconnected IoT components, namely, a multiantenna WiFi installation (2.45 GHz), a sub-THz imaging camera (100 GHz), a network of radars (122 GHz) and infrared sensors (813). The performance of the proposed IoT platform is validated through real use case scenarios inside a pilot industrial plant in which protective humanrobot distance must be guaranteed considering latency and detection uncertainties.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
