Overapproximative arm occupancy prediction for human-robot co-existence built from archetypal movements

Abstract

Human motion is fast and hard to predict. To implement a provably safe collision-avoidance strategy for robots in collaborative spaces with humans, an overapproximative prediction of the occupancy of the human is required, which needs to be calculated faster than real time. We present a method for computing volumes containing the entire possible future occupancy of the human, given its state, faster than real time. The dynamic model of the human is built from analysing a set of archetypal movements performed by test subjects. The occupancy prediction is tested on a publicly available database of motion capture data, and shown to be overapproximative for all movements relating to everyday activities, sport and dance. Our novel algorithm is useful to guarantee safety in human-robot collaboration scenarios.