Toward safe separation distance monitoring from RGB-D sensors in human-robot interaction
This addresses safety for human operators in less constrained environments, but it is incremental as it builds on existing monitoring approaches with added detail and tunability.
The paper tackled the problem of ensuring safety in human-robot interaction by developing a framework for detailed separation distance monitoring, which assesses distances pair-wise between robot and human keypoints, as demonstrated using a Nao robot and RealSense sensor.
The interaction of humans and robots in less constrained environments gains a lot of attention lately and the safety of such interaction is of utmost importance. Two ways of risk assessment are prescribed by recent safety standards: (i) power and force limiting and (ii) speed and separation monitoring. Unlike typical solutions in the industry that are restricted to mere safety zone monitoring, we present a framework that realizes separation distance monitoring between a robot and a human operator in a detailed, yet versatile, transparent, and tunable fashion. The separation distance is assessed pair-wise for all keypoints on the robot and the human body and as such can be selectively modified to account for specific conditions. The operation of this framework is illustrated on a Nao humanoid robot interacting with a human partner perceived by a RealSense RGB-D sensor and employing the OpenPose human skeleton estimation algorithm.