Workspace monitoring is a critical hw/sw component of modern industrial work cells or in service robotics scenarios, where human operators share their workspace with robots. Reliability of human detection is a major requirement not only for safety purposes but also to avoid unnecessary robot stops or slowdowns in case of false positives. The present paper introduces a novel multimodal perception system for human tracking in shared workspaces based on the fusion of depth and thermal images. A machine learning approach is pursued to achieve reliable detection performance in multi-robot collaborative systems. Robust experimental results are finally demonstrated on a real robotic work cell.
A multimodal perception system for detection of human operators in robotic work cells
Costanzo M.;De Maria G.;Natale C.
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2019
Abstract
Workspace monitoring is a critical hw/sw component of modern industrial work cells or in service robotics scenarios, where human operators share their workspace with robots. Reliability of human detection is a major requirement not only for safety purposes but also to avoid unnecessary robot stops or slowdowns in case of false positives. The present paper introduces a novel multimodal perception system for human tracking in shared workspaces based on the fusion of depth and thermal images. A machine learning approach is pursued to achieve reliable detection performance in multi-robot collaborative systems. Robust experimental results are finally demonstrated on a real robotic work cell.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
