  In this work, we support experts in the safety domain with safer dismantling
of drug labs, by deploying robots for the initial inspection. Being able to act
on the discovered environment is key to enabling this (semi-)autonomous
inspection, e.g. to open doors or take a closer at suspicious items. Our
approach addresses this with a novel environmental representation, the
Behavior-Oriented Situational Graph, where we extend on the classical
situational graph by merging a perception-driven backbone with prior actionable
knowledge via a situational affordance schema. Linking situations to robot
behaviors facilitates both autonomous mission planning and situational
understanding of the operator. Planning over the graph is easier and faster,
since it directly incorporates actionable information, which is critical for
online mission systems. Moreover, the representation allows the human operator
to seamlessly transition between different levels of autonomy of the robot,
from remote control to behavior execution to full autonomous exploration. We
test the effectiveness of our approach in a real-world drug lab scenario at a
Dutch police training facility using a mobile Spot robot and use the results to
iterate on the system design.
