A Multimodal Perception System for Detection of Human Operators in Robotic Work Cells

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.