HistoDepth - Novel Depth Perception for Safe Collaborative Robots

Abstract

The trend towards Industry 4.0 demands an increasing flexibility of system configurations including more agile collaborative human-robot systems that can quickly adapt to new tasks and missions. At the same time, state-of-the-art safety solution for many industrial robots is the use of barriers around the robot to limit the exposure to human co-workers. As these barriers cannot be reconfigured easily, it considerably limits the flexibility of many robotic solutions. Thus, new more agile safety solutions are required, and these require new, robust perception systems that can ensure detection of all safety relevant objects. In this paper, we propose a novel depth perception approach called HistoDepth, which addresses this need. It uses depth sensors that are mounted at fixed, static positions outside of the workspace and tracks each pixel measurement statistically over time. This enables our solution to differentiate static scene elements from dynamic (potentially hazardous) elements and to enforce a robot safety maneuver upon detection of a critical object. Moreover, it can adapt automatically to changing noise or scene configurations without user input or the need for setup changes. Using an UR5 robot arm, we demonstrate that this solution can enable new agile and flexible safety concepts for collaborative robots.