This paper presents a decentralised human-aware navigation algorithm for shared human–robot work-spaces based on the velocity obstacles paradigm. By extending our previous work on collision avoidance, we are able to include and avoid static and dynamic obstacles, no matter whether they are induced by other robots and humans passing through. Using various cost maps and Monte Carlo sampling with different cost factors accounting for humans and robots, the approach allows human workers to use the same navigation space as robots. It does not rely on any external positioning sensors and shows its feasibility even in densely packed environments.