Abstract

For adoption of Autonomous Mobile Robots (AMR) across a breadth of industries,
they must navigate around humans in a way which is safe and which humans perceive as safe, but without greatly compromising efficiency. This work proposes a novel
classifier of the Human-Robot Spatial Interaction (HRSI) situation of an interacting
human and robot, to be applied in Human-Aware Navigation (HAN) to account for
situational context. A classifier comprised of per-situation Hidden Markov Models
is developed, and trained with sequences of states in Qualitative Trajectory Calculus, representing relative human and robot movements in various HRSI situations.
This multi-HMM HRSI situation classifier is created as a component of the safety
stack for the EU Horizon 2020 ILIAD Project, and the theoretical foundation and
implementation of this system is described, along with the results of a HRI study
that evaluates the classification performance of this work’s novel classifier. The aim
of this work is to demonstrate accurate continuous real-time classification of a set
of socially legible HRSI situations that occur when a proximate human and heavy
industrial robot are moving through a shared space. High classification performance
is demonstrated, with future work currently being conducted by ILIAD colleagues to
test a complete HAN system that employs this real-time situation classification to
apply situational qualitative motion constraints, as well as testing the ILIAD safety
stack as a whole.